Analysis of 111/999 NHS calls and 111 online reports
Quentin Leclerc, Emily Nightingale, Samuel Abbott & Thibaut Jombart, for CMMID
Thursday 21 January 2021
1 Data preparation
1.1 Outline
Load scripts: loads libraries and useful scripts used in the analyses; all
.Rfiles contained inscriptsat the root of the factory are automatically loadedLoad data: imports datasets, and may contain some ad hoc changes to the data such as specific data cleaning (not used in other reports), new variables used in the analyses, etc.
1.2 Load packages
library(reportfactory)
library(here)
library(rio)
library(tidyverse)
library(incidence)
library(distcrete)
library(epitrix)
library(earlyR)
library(projections)
library(linelist)
library(remotes)
library(janitor)
library(kableExtra)
library(DT)
library(cyphr)
library(chngpt)
library(lubridate)
library(ggpubr)
library(ggnewscale)1.3 Load scripts
These scripts will load:
- all scripts stored as
.Rfiles inside/scripts/ - all scripts stored as
.Rfiles inside/src/
These scripts also contain routines to access the latest clean encrypted data (see next section).
1.4 Load clean data
We import the latest NHS pathways data:
x <- import_pathways() %>%
as_tibble()
x
## [90m# A tibble: 410,726 x 11[39m
## site_type date sex age ccg_code ccg_name count postcode nhs_region
## [3m[90m<chr>[39m[23m [3m[90m<date>[39m[23m [3m[90m<chr>[39m[23m [3m[90m<chr>[39m[23m [3m[90m<chr>[39m[23m [3m[90m<chr>[39m[23m [3m[90m<int>[39m[23m [3m[90m<chr>[39m[23m [3m[90m<chr>[39m[23m
## [90m 1[39m 111 2020-03-18 fema… miss… e380000… nhs_glo… 1 gl34fe South West
## [90m 2[39m 111 2020-03-18 fema… miss… e380001… nhs_sou… 1 ne325nn North Eas…
## [90m 3[39m 111 2020-03-18 fema… 0-18 e380000… nhs_air… 8 bd57jr North Eas…
## [90m 4[39m 111 2020-03-18 fema… 0-18 e380000… nhs_ash… 7 tn254ab South East
## [90m 5[39m 111 2020-03-18 fema… 0-18 e380000… nhs_bar… 35 rm13ae London
## [90m 6[39m 111 2020-03-18 fema… 0-18 e380000… nhs_bar… 9 n111np London
## [90m 7[39m 111 2020-03-18 fema… 0-18 e380000… nhs_bar… 11 s752py North Eas…
## [90m 8[39m 111 2020-03-18 fema… 0-18 e380000… nhs_bas… 19 ss143hg East of E…
## [90m 9[39m 111 2020-03-18 fema… 0-18 e380000… nhs_bas… 6 dn227xf North Eas…
## [90m10[39m 111 2020-03-18 fema… 0-18 e380000… nhs_bat… 9 ba25rp South West
## [90m# … with 410,716 more rows, and 2 more variables: day [3m[90m<int>[90m[23m, weekday [3m[90m<fct>[90m[23m[39mWe also import demographics data for NHS regions in England, used later in our analysis:
path <- here::here("data", "csv", "nhs_region_population_2018.csv")
nhs_region_pop <- rio::import(path) %>%
mutate(nhs_region = str_to_title(gsub("_"," ",nhs_region)))
nhs_region_pop$nhs_region <- gsub(" Of ", " of ", nhs_region_pop$nhs_region)
nhs_region_pop$nhs_region <- gsub(" And ", " and ", nhs_region_pop$nhs_region)
nhs_region_pop
## nhs_region variable value
## 1 North West 0-18 0.22538599
## 2 North East and Yorkshire 0-18 0.21876449
## 3 Midlands 0-18 0.22564656
## 4 East of England 0-18 0.22810783
## 5 London 0-18 0.23764782
## 6 South East 0-18 0.22458811
## 7 South West 0-18 0.20799797
## 8 North West 19-69 0.64274078
## 9 North East and Yorkshire 19-69 0.64437753
## 10 Midlands 19-69 0.63876675
## 11 East of England 19-69 0.63034229
## 12 London 19-69 0.67820084
## 13 South East 19-69 0.63267336
## 14 South West 19-69 0.63176131
## 15 North West 70-120 0.13187323
## 16 North East and Yorkshire 70-120 0.13685797
## 17 Midlands 70-120 0.13558669
## 18 East of England 70-120 0.14154988
## 19 London 70-120 0.08415135
## 20 South East 70-120 0.14273853
## 21 South West 70-120 0.16024072Finally, we import publically available deaths per NHS region:
dth <- import_deaths() %>%
mutate(nhs_region = str_to_title(gsub("_"," ",nhs_region)))
#truncation to account for reporting delay
delay_max <- 21
dth$nhs_region <- gsub(" Of ", " of ", dth$nhs_region)
dth$nhs_region <- gsub(" And ", " and ", dth$nhs_region)
dth
## date_report nhs_region deaths
## 1 2020-03-01 East of England 0
## 2 2020-03-02 East of England 1
## 3 2020-03-03 East of England 0
## 4 2020-03-04 East of England 0
## 5 2020-03-05 East of England 0
## 6 2020-03-06 East of England 1
## 7 2020-03-07 East of England 0
## 8 2020-03-08 East of England 0
## 9 2020-03-09 East of England 1
## 10 2020-03-10 East of England 0
## 11 2020-03-11 East of England 0
## 12 2020-03-12 East of England 0
## 13 2020-03-13 East of England 1
## 14 2020-03-14 East of England 2
## 15 2020-03-15 East of England 2
## 16 2020-03-16 East of England 1
## 17 2020-03-17 East of England 1
## 18 2020-03-18 East of England 5
## 19 2020-03-19 East of England 4
## 20 2020-03-20 East of England 2
## 21 2020-03-21 East of England 11
## 22 2020-03-22 East of England 12
## 23 2020-03-23 East of England 11
## 24 2020-03-24 East of England 19
## 25 2020-03-25 East of England 26
## 26 2020-03-26 East of England 36
## 27 2020-03-27 East of England 38
## 28 2020-03-28 East of England 28
## 29 2020-03-29 East of England 43
## 30 2020-03-30 East of England 45
## 31 2020-03-31 East of England 70
## 32 2020-04-01 East of England 62
## 33 2020-04-02 East of England 65
## 34 2020-04-03 East of England 80
## 35 2020-04-04 East of England 71
## 36 2020-04-05 East of England 76
## 37 2020-04-06 East of England 71
## 38 2020-04-07 East of England 93
## 39 2020-04-08 East of England 111
## 40 2020-04-09 East of England 87
## 41 2020-04-10 East of England 74
## 42 2020-04-11 East of England 92
## 43 2020-04-12 East of England 100
## 44 2020-04-13 East of England 78
## 45 2020-04-14 East of England 61
## 46 2020-04-15 East of England 82
## 47 2020-04-16 East of England 74
## 48 2020-04-17 East of England 86
## 49 2020-04-18 East of England 64
## 50 2020-04-19 East of England 67
## 51 2020-04-20 East of England 67
## 52 2020-04-21 East of England 75
## 53 2020-04-22 East of England 67
## 54 2020-04-23 East of England 49
## 55 2020-04-24 East of England 66
## 56 2020-04-25 East of England 54
## 57 2020-04-26 East of England 48
## 58 2020-04-27 East of England 46
## 59 2020-04-28 East of England 58
## 60 2020-04-29 East of England 32
## 61 2020-04-30 East of England 45
## 62 2020-05-01 East of England 49
## 63 2020-05-02 East of England 29
## 64 2020-05-03 East of England 41
## 65 2020-05-04 East of England 19
## 66 2020-05-05 East of England 36
## 67 2020-05-06 East of England 31
## 68 2020-05-07 East of England 33
## 69 2020-05-08 East of England 33
## 70 2020-05-09 East of England 29
## 71 2020-05-10 East of England 22
## 72 2020-05-11 East of England 18
## 73 2020-05-12 East of England 21
## 74 2020-05-13 East of England 27
## 75 2020-05-14 East of England 26
## 76 2020-05-15 East of England 19
## 77 2020-05-16 East of England 26
## 78 2020-05-17 East of England 17
## 79 2020-05-18 East of England 25
## 80 2020-05-19 East of England 15
## 81 2020-05-20 East of England 26
## 82 2020-05-21 East of England 21
## 83 2020-05-22 East of England 13
## 84 2020-05-23 East of England 12
## 85 2020-05-24 East of England 17
## 86 2020-05-25 East of England 25
## 87 2020-05-26 East of England 14
## 88 2020-05-27 East of England 12
## 89 2020-05-28 East of England 17
## 90 2020-05-29 East of England 16
## 91 2020-05-30 East of England 9
## 92 2020-05-31 East of England 8
## 93 2020-06-01 East of England 17
## 94 2020-06-02 East of England 14
## 95 2020-06-03 East of England 10
## 96 2020-06-04 East of England 7
## 97 2020-06-05 East of England 14
## 98 2020-06-06 East of England 5
## 99 2020-06-07 East of England 9
## 100 2020-06-08 East of England 7
## 101 2020-06-09 East of England 6
## 102 2020-06-10 East of England 8
## 103 2020-06-11 East of England 1
## 104 2020-06-12 East of England 9
## 105 2020-06-13 East of England 5
## 106 2020-06-14 East of England 4
## 107 2020-06-15 East of England 8
## 108 2020-06-16 East of England 3
## 109 2020-06-17 East of England 7
## 110 2020-06-18 East of England 4
## 111 2020-06-19 East of England 7
## 112 2020-06-20 East of England 4
## 113 2020-06-21 East of England 3
## 114 2020-06-22 East of England 6
## 115 2020-06-23 East of England 5
## 116 2020-06-24 East of England 4
## 117 2020-06-25 East of England 1
## 118 2020-06-26 East of England 5
## 119 2020-06-27 East of England 6
## 120 2020-06-28 East of England 8
## 121 2020-06-29 East of England 4
## 122 2020-06-30 East of England 5
## 123 2020-07-01 East of England 2
## 124 2020-07-02 East of England 5
## 125 2020-07-03 East of England 0
## 126 2020-07-04 East of England 3
## 127 2020-07-05 East of England 1
## 128 2020-07-06 East of England 2
## 129 2020-07-07 East of England 2
## 130 2020-07-08 East of England 0
## 131 2020-07-09 East of England 8
## 132 2020-07-10 East of England 4
## 133 2020-07-11 East of England 2
## 134 2020-07-12 East of England 1
## 135 2020-07-13 East of England 8
## 136 2020-07-14 East of England 2
## 137 2020-07-15 East of England 0
## 138 2020-07-16 East of England 0
## 139 2020-07-17 East of England 0
## 140 2020-07-18 East of England 0
## 141 2020-07-19 East of England 1
## 142 2020-07-20 East of England 1
## 143 2020-07-21 East of England 1
## 144 2020-07-22 East of England 2
## 145 2020-07-23 East of England 1
## 146 2020-07-24 East of England 1
## 147 2020-07-25 East of England 0
## 148 2020-07-26 East of England 1
## 149 2020-07-27 East of England 1
## 150 2020-07-28 East of England 2
## 151 2020-07-29 East of England 0
## 152 2020-07-30 East of England 0
## 153 2020-07-31 East of England 1
## 154 2020-08-01 East of England 0
## 155 2020-08-02 East of England 0
## 156 2020-08-03 East of England 0
## 157 2020-08-04 East of England 1
## 158 2020-08-05 East of England 1
## 159 2020-08-06 East of England 0
## 160 2020-08-07 East of England 1
## 161 2020-08-08 East of England 0
## 162 2020-08-09 East of England 0
## 163 2020-08-10 East of England 1
## 164 2020-08-11 East of England 2
## 165 2020-08-12 East of England 1
## 166 2020-08-13 East of England 0
## 167 2020-08-14 East of England 1
## 168 2020-08-15 East of England 1
## 169 2020-08-16 East of England 0
## 170 2020-08-17 East of England 0
## 171 2020-08-18 East of England 2
## 172 2020-08-19 East of England 1
## 173 2020-08-20 East of England 1
## 174 2020-08-21 East of England 0
## 175 2020-08-22 East of England 1
## 176 2020-08-23 East of England 1
## 177 2020-08-24 East of England 0
## 178 2020-08-25 East of England 0
## 179 2020-08-26 East of England 1
## 180 2020-08-27 East of England 1
## 181 2020-08-28 East of England 0
## 182 2020-08-29 East of England 0
## 183 2020-08-30 East of England 0
## 184 2020-08-31 East of England 0
## 185 2020-09-01 East of England 0
## 186 2020-09-02 East of England 0
## 187 2020-09-03 East of England 1
## 188 2020-09-04 East of England 1
## 189 2020-09-05 East of England 0
## 190 2020-09-06 East of England 1
## 191 2020-09-07 East of England 0
## 192 2020-09-08 East of England 0
## 193 2020-09-09 East of England 0
## 194 2020-09-10 East of England 0
## 195 2020-09-11 East of England 0
## 196 2020-09-12 East of England 0
## 197 2020-09-13 East of England 1
## 198 2020-09-14 East of England 1
## 199 2020-09-15 East of England 0
## 200 2020-09-16 East of England 0
## 201 2020-09-17 East of England 0
## 202 2020-09-18 East of England 0
## 203 2020-09-19 East of England 0
## 204 2020-09-20 East of England 2
## 205 2020-09-21 East of England 0
## 206 2020-09-22 East of England 2
## 207 2020-09-23 East of England 1
## 208 2020-09-24 East of England 0
## 209 2020-09-25 East of England 1
## 210 2020-09-26 East of England 1
## 211 2020-09-27 East of England 1
## 212 2020-09-28 East of England 2
## 213 2020-09-29 East of England 2
## 214 2020-09-30 East of England 2
## 215 2020-10-01 East of England 2
## 216 2020-10-02 East of England 1
## 217 2020-10-03 East of England 1
## 218 2020-10-04 East of England 0
## 219 2020-10-05 East of England 0
## 220 2020-10-06 East of England 4
## 221 2020-10-07 East of England 6
## 222 2020-10-08 East of England 3
## 223 2020-10-09 East of England 1
## 224 2020-10-10 East of England 6
## 225 2020-10-11 East of England 2
## 226 2020-10-12 East of England 2
## 227 2020-10-13 East of England 1
## 228 2020-10-14 East of England 3
## 229 2020-10-15 East of England 4
## 230 2020-10-16 East of England 5
## 231 2020-10-17 East of England 6
## 232 2020-10-18 East of England 7
## 233 2020-10-19 East of England 5
## 234 2020-10-20 East of England 9
## 235 2020-10-21 East of England 7
## 236 2020-10-22 East of England 7
## 237 2020-10-23 East of England 14
## 238 2020-10-24 East of England 1
## 239 2020-10-25 East of England 10
## 240 2020-10-26 East of England 10
## 241 2020-10-27 East of England 8
## 242 2020-10-28 East of England 12
## 243 2020-10-29 East of England 10
## 244 2020-10-30 East of England 12
## 245 2020-10-31 East of England 15
## 246 2020-11-01 East of England 14
## 247 2020-11-02 East of England 9
## 248 2020-11-03 East of England 14
## 249 2020-11-04 East of England 11
## 250 2020-11-05 East of England 12
## 251 2020-11-06 East of England 18
## 252 2020-11-07 East of England 10
## 253 2020-11-08 East of England 13
## 254 2020-11-09 East of England 16
## 255 2020-11-10 East of England 26
## 256 2020-11-11 East of England 14
## 257 2020-11-12 East of England 14
## 258 2020-11-13 East of England 21
## 259 2020-11-14 East of England 19
## 260 2020-11-15 East of England 13
## 261 2020-11-16 East of England 11
## 262 2020-11-17 East of England 17
## 263 2020-11-18 East of England 19
## 264 2020-11-19 East of England 23
## 265 2020-11-20 East of England 24
## 266 2020-11-21 East of England 19
## 267 2020-11-22 East of England 21
## 268 2020-11-23 East of England 18
## 269 2020-11-24 East of England 21
## 270 2020-11-25 East of England 19
## 271 2020-11-26 East of England 19
## 272 2020-11-27 East of England 14
## 273 2020-11-28 East of England 28
## 274 2020-11-29 East of England 19
## 275 2020-11-30 East of England 22
## 276 2020-12-01 East of England 25
## 277 2020-12-02 East of England 18
## 278 2020-12-03 East of England 23
## 279 2020-12-04 East of England 24
## 280 2020-12-05 East of England 25
## 281 2020-12-06 East of England 21
## 282 2020-12-07 East of England 16
## 283 2020-12-08 East of England 26
## 284 2020-12-09 East of England 19
## 285 2020-12-10 East of England 32
## 286 2020-12-11 East of England 32
## 287 2020-12-12 East of England 27
## 288 2020-12-13 East of England 24
## 289 2020-12-14 East of England 31
## 290 2020-12-15 East of England 35
## 291 2020-12-16 East of England 29
## 292 2020-12-17 East of England 41
## 293 2020-12-18 East of England 44
## 294 2020-12-19 East of England 54
## 295 2020-12-20 East of England 50
## 296 2020-12-21 East of England 64
## 297 2020-12-22 East of England 52
## 298 2020-12-23 East of England 60
## 299 2020-12-24 East of England 54
## 300 2020-12-25 East of England 56
## 301 2020-12-26 East of England 61
## 302 2020-12-27 East of England 53
## 303 2020-12-28 East of England 70
## 304 2020-12-29 East of England 49
## 305 2020-12-30 East of England 69
## 306 2020-12-31 East of England 79
## 307 2021-01-01 East of England 85
## 308 2021-01-02 East of England 63
## 309 2021-01-03 East of England 75
## 310 2021-01-04 East of England 79
## 311 2021-01-05 East of England 99
## 312 2021-01-06 East of England 88
## 313 2021-01-07 East of England 107
## 314 2021-01-08 East of England 82
## 315 2021-01-09 East of England 114
## 316 2021-01-10 East of England 114
## 317 2021-01-11 East of England 114
## 318 2021-01-12 East of England 109
## 319 2021-01-13 East of England 106
## 320 2021-01-14 East of England 92
## 321 2021-01-15 East of England 82
## 322 2021-01-16 East of England 92
## 323 2021-01-17 East of England 89
## 324 2021-01-18 East of England 62
## 325 2021-01-19 East of England 17
## 326 2020-03-01 London 0
## 327 2020-03-02 London 0
## 328 2020-03-03 London 0
## 329 2020-03-04 London 0
## 330 2020-03-05 London 0
## 331 2020-03-06 London 1
## 332 2020-03-07 London 0
## 333 2020-03-08 London 0
## 334 2020-03-09 London 1
## 335 2020-03-10 London 0
## 336 2020-03-11 London 5
## 337 2020-03-12 London 6
## 338 2020-03-13 London 10
## 339 2020-03-14 London 13
## 340 2020-03-15 London 9
## 341 2020-03-16 London 15
## 342 2020-03-17 London 23
## 343 2020-03-18 London 28
## 344 2020-03-19 London 25
## 345 2020-03-20 London 44
## 346 2020-03-21 London 49
## 347 2020-03-22 London 54
## 348 2020-03-23 London 63
## 349 2020-03-24 London 86
## 350 2020-03-25 London 112
## 351 2020-03-26 London 130
## 352 2020-03-27 London 130
## 353 2020-03-28 London 123
## 354 2020-03-29 London 145
## 355 2020-03-30 London 151
## 356 2020-03-31 London 183
## 357 2020-04-01 London 202
## 358 2020-04-02 London 191
## 359 2020-04-03 London 199
## 360 2020-04-04 London 231
## 361 2020-04-05 London 195
## 362 2020-04-06 London 198
## 363 2020-04-07 London 220
## 364 2020-04-08 London 239
## 365 2020-04-09 London 207
## 366 2020-04-10 London 171
## 367 2020-04-11 London 178
## 368 2020-04-12 London 159
## 369 2020-04-13 London 166
## 370 2020-04-14 London 143
## 371 2020-04-15 London 143
## 372 2020-04-16 London 140
## 373 2020-04-17 London 101
## 374 2020-04-18 London 101
## 375 2020-04-19 London 103
## 376 2020-04-20 London 96
## 377 2020-04-21 London 96
## 378 2020-04-22 London 109
## 379 2020-04-23 London 77
## 380 2020-04-24 London 71
## 381 2020-04-25 London 58
## 382 2020-04-26 London 53
## 383 2020-04-27 London 52
## 384 2020-04-28 London 44
## 385 2020-04-29 London 45
## 386 2020-04-30 London 40
## 387 2020-05-01 London 41
## 388 2020-05-02 London 41
## 389 2020-05-03 London 36
## 390 2020-05-04 London 30
## 391 2020-05-05 London 25
## 392 2020-05-06 London 37
## 393 2020-05-07 London 37
## 394 2020-05-08 London 31
## 395 2020-05-09 London 23
## 396 2020-05-10 London 26
## 397 2020-05-11 London 18
## 398 2020-05-12 London 18
## 399 2020-05-13 London 17
## 400 2020-05-14 London 20
## 401 2020-05-15 London 19
## 402 2020-05-16 London 14
## 403 2020-05-17 London 15
## 404 2020-05-18 London 11
## 405 2020-05-19 London 14
## 406 2020-05-20 London 19
## 407 2020-05-21 London 12
## 408 2020-05-22 London 10
## 409 2020-05-23 London 6
## 410 2020-05-24 London 7
## 411 2020-05-25 London 9
## 412 2020-05-26 London 14
## 413 2020-05-27 London 7
## 414 2020-05-28 London 8
## 415 2020-05-29 London 7
## 416 2020-05-30 London 12
## 417 2020-05-31 London 6
## 418 2020-06-01 London 10
## 419 2020-06-02 London 8
## 420 2020-06-03 London 6
## 421 2020-06-04 London 8
## 422 2020-06-05 London 4
## 423 2020-06-06 London 0
## 424 2020-06-07 London 5
## 425 2020-06-08 London 5
## 426 2020-06-09 London 5
## 427 2020-06-10 London 8
## 428 2020-06-11 London 5
## 429 2020-06-12 London 3
## 430 2020-06-13 London 3
## 431 2020-06-14 London 3
## 432 2020-06-15 London 1
## 433 2020-06-16 London 2
## 434 2020-06-17 London 1
## 435 2020-06-18 London 2
## 436 2020-06-19 London 5
## 437 2020-06-20 London 3
## 438 2020-06-21 London 4
## 439 2020-06-22 London 2
## 440 2020-06-23 London 1
## 441 2020-06-24 London 4
## 442 2020-06-25 London 3
## 443 2020-06-26 London 2
## 444 2020-06-27 London 1
## 445 2020-06-28 London 2
## 446 2020-06-29 London 2
## 447 2020-06-30 London 1
## 448 2020-07-01 London 3
## 449 2020-07-02 London 2
## 450 2020-07-03 London 2
## 451 2020-07-04 London 1
## 452 2020-07-05 London 3
## 453 2020-07-06 London 2
## 454 2020-07-07 London 1
## 455 2020-07-08 London 3
## 456 2020-07-09 London 4
## 457 2020-07-10 London 0
## 458 2020-07-11 London 1
## 459 2020-07-12 London 1
## 460 2020-07-13 London 1
## 461 2020-07-14 London 0
## 462 2020-07-15 London 2
## 463 2020-07-16 London 0
## 464 2020-07-17 London 0
## 465 2020-07-18 London 2
## 466 2020-07-19 London 0
## 467 2020-07-20 London 0
## 468 2020-07-21 London 1
## 469 2020-07-22 London 0
## 470 2020-07-23 London 2
## 471 2020-07-24 London 0
## 472 2020-07-25 London 1
## 473 2020-07-26 London 0
## 474 2020-07-27 London 1
## 475 2020-07-28 London 0
## 476 2020-07-29 London 0
## 477 2020-07-30 London 1
## 478 2020-07-31 London 0
## 479 2020-08-01 London 0
## 480 2020-08-02 London 3
## 481 2020-08-03 London 0
## 482 2020-08-04 London 0
## 483 2020-08-05 London 0
## 484 2020-08-06 London 1
## 485 2020-08-07 London 0
## 486 2020-08-08 London 0
## 487 2020-08-09 London 0
## 488 2020-08-10 London 0
## 489 2020-08-11 London 1
## 490 2020-08-12 London 0
## 491 2020-08-13 London 2
## 492 2020-08-14 London 0
## 493 2020-08-15 London 0
## 494 2020-08-16 London 0
## 495 2020-08-17 London 1
## 496 2020-08-18 London 1
## 497 2020-08-19 London 0
## 498 2020-08-20 London 1
## 499 2020-08-21 London 0
## 500 2020-08-22 London 0
## 501 2020-08-23 London 0
## 502 2020-08-24 London 1
## 503 2020-08-25 London 1
## 504 2020-08-26 London 0
## 505 2020-08-27 London 0
## 506 2020-08-28 London 0
## 507 2020-08-29 London 0
## 508 2020-08-30 London 0
## 509 2020-08-31 London 1
## 510 2020-09-01 London 0
## 511 2020-09-02 London 1
## 512 2020-09-03 London 1
## 513 2020-09-04 London 0
## 514 2020-09-05 London 0
## 515 2020-09-06 London 2
## 516 2020-09-07 London 0
## 517 2020-09-08 London 0
## 518 2020-09-09 London 0
## 519 2020-09-10 London 2
## 520 2020-09-11 London 1
## 521 2020-09-12 London 1
## 522 2020-09-13 London 0
## 523 2020-09-14 London 0
## 524 2020-09-15 London 1
## 525 2020-09-16 London 2
## 526 2020-09-17 London 2
## 527 2020-09-18 London 1
## 528 2020-09-19 London 3
## 529 2020-09-20 London 3
## 530 2020-09-21 London 2
## 531 2020-09-22 London 6
## 532 2020-09-23 London 4
## 533 2020-09-24 London 3
## 534 2020-09-25 London 1
## 535 2020-09-26 London 1
## 536 2020-09-27 London 1
## 537 2020-09-28 London 3
## 538 2020-09-29 London 7
## 539 2020-09-30 London 6
## 540 2020-10-01 London 4
## 541 2020-10-02 London 1
## 542 2020-10-03 London 3
## 543 2020-10-04 London 2
## 544 2020-10-05 London 7
## 545 2020-10-06 London 4
## 546 2020-10-07 London 6
## 547 2020-10-08 London 6
## 548 2020-10-09 London 7
## 549 2020-10-10 London 3
## 550 2020-10-11 London 5
## 551 2020-10-12 London 7
## 552 2020-10-13 London 4
## 553 2020-10-14 London 6
## 554 2020-10-15 London 13
## 555 2020-10-16 London 6
## 556 2020-10-17 London 2
## 557 2020-10-18 London 5
## 558 2020-10-19 London 11
## 559 2020-10-20 London 8
## 560 2020-10-21 London 14
## 561 2020-10-22 London 12
## 562 2020-10-23 London 7
## 563 2020-10-24 London 18
## 564 2020-10-25 London 10
## 565 2020-10-26 London 10
## 566 2020-10-27 London 12
## 567 2020-10-28 London 23
## 568 2020-10-29 London 14
## 569 2020-10-30 London 17
## 570 2020-10-31 London 7
## 571 2020-11-01 London 17
## 572 2020-11-02 London 16
## 573 2020-11-03 London 10
## 574 2020-11-04 London 18
## 575 2020-11-05 London 17
## 576 2020-11-06 London 12
## 577 2020-11-07 London 21
## 578 2020-11-08 London 15
## 579 2020-11-09 London 28
## 580 2020-11-10 London 14
## 581 2020-11-11 London 15
## 582 2020-11-12 London 16
## 583 2020-11-13 London 14
## 584 2020-11-14 London 21
## 585 2020-11-15 London 18
## 586 2020-11-16 London 29
## 587 2020-11-17 London 29
## 588 2020-11-18 London 23
## 589 2020-11-19 London 24
## 590 2020-11-20 London 20
## 591 2020-11-21 London 18
## 592 2020-11-22 London 29
## 593 2020-11-23 London 19
## 594 2020-11-24 London 26
## 595 2020-11-25 London 30
## 596 2020-11-26 London 25
## 597 2020-11-27 London 28
## 598 2020-11-28 London 23
## 599 2020-11-29 London 40
## 600 2020-11-30 London 19
## 601 2020-12-01 London 28
## 602 2020-12-02 London 30
## 603 2020-12-03 London 27
## 604 2020-12-04 London 29
## 605 2020-12-05 London 24
## 606 2020-12-06 London 24
## 607 2020-12-07 London 29
## 608 2020-12-08 London 35
## 609 2020-12-09 London 27
## 610 2020-12-10 London 29
## 611 2020-12-11 London 26
## 612 2020-12-12 London 33
## 613 2020-12-13 London 33
## 614 2020-12-14 London 37
## 615 2020-12-15 London 49
## 616 2020-12-16 London 35
## 617 2020-12-17 London 56
## 618 2020-12-18 London 40
## 619 2020-12-19 London 39
## 620 2020-12-20 London 52
## 621 2020-12-21 London 56
## 622 2020-12-22 London 56
## 623 2020-12-23 London 57
## 624 2020-12-24 London 64
## 625 2020-12-25 London 81
## 626 2020-12-26 London 82
## 627 2020-12-27 London 90
## 628 2020-12-28 London 88
## 629 2020-12-29 London 107
## 630 2020-12-30 London 98
## 631 2020-12-31 London 107
## 632 2021-01-01 London 112
## 633 2021-01-02 London 124
## 634 2021-01-03 London 106
## 635 2021-01-04 London 147
## 636 2021-01-05 London 141
## 637 2021-01-06 London 144
## 638 2021-01-07 London 154
## 639 2021-01-08 London 138
## 640 2021-01-09 London 143
## 641 2021-01-10 London 158
## 642 2021-01-11 London 157
## 643 2021-01-12 London 165
## 644 2021-01-13 London 154
## 645 2021-01-14 London 147
## 646 2021-01-15 London 131
## 647 2021-01-16 London 112
## 648 2021-01-17 London 121
## 649 2021-01-18 London 106
## 650 2021-01-19 London 16
## 651 2020-03-01 Midlands 0
## 652 2020-03-02 Midlands 0
## 653 2020-03-03 Midlands 1
## 654 2020-03-04 Midlands 0
## 655 2020-03-05 Midlands 0
## 656 2020-03-06 Midlands 0
## 657 2020-03-07 Midlands 0
## 658 2020-03-08 Midlands 2
## 659 2020-03-09 Midlands 1
## 660 2020-03-10 Midlands 0
## 661 2020-03-11 Midlands 2
## 662 2020-03-12 Midlands 6
## 663 2020-03-13 Midlands 5
## 664 2020-03-14 Midlands 4
## 665 2020-03-15 Midlands 5
## 666 2020-03-16 Midlands 11
## 667 2020-03-17 Midlands 8
## 668 2020-03-18 Midlands 13
## 669 2020-03-19 Midlands 8
## 670 2020-03-20 Midlands 28
## 671 2020-03-21 Midlands 13
## 672 2020-03-22 Midlands 31
## 673 2020-03-23 Midlands 33
## 674 2020-03-24 Midlands 41
## 675 2020-03-25 Midlands 48
## 676 2020-03-26 Midlands 64
## 677 2020-03-27 Midlands 72
## 678 2020-03-28 Midlands 89
## 679 2020-03-29 Midlands 92
## 680 2020-03-30 Midlands 90
## 681 2020-03-31 Midlands 123
## 682 2020-04-01 Midlands 140
## 683 2020-04-02 Midlands 142
## 684 2020-04-03 Midlands 124
## 685 2020-04-04 Midlands 151
## 686 2020-04-05 Midlands 164
## 687 2020-04-06 Midlands 140
## 688 2020-04-07 Midlands 123
## 689 2020-04-08 Midlands 186
## 690 2020-04-09 Midlands 140
## 691 2020-04-10 Midlands 127
## 692 2020-04-11 Midlands 142
## 693 2020-04-12 Midlands 139
## 694 2020-04-13 Midlands 120
## 695 2020-04-14 Midlands 116
## 696 2020-04-15 Midlands 147
## 697 2020-04-16 Midlands 102
## 698 2020-04-17 Midlands 118
## 699 2020-04-18 Midlands 115
## 700 2020-04-19 Midlands 93
## 701 2020-04-20 Midlands 107
## 702 2020-04-21 Midlands 86
## 703 2020-04-22 Midlands 78
## 704 2020-04-23 Midlands 103
## 705 2020-04-24 Midlands 79
## 706 2020-04-25 Midlands 72
## 707 2020-04-26 Midlands 81
## 708 2020-04-27 Midlands 74
## 709 2020-04-28 Midlands 68
## 710 2020-04-29 Midlands 53
## 711 2020-04-30 Midlands 56
## 712 2020-05-01 Midlands 64
## 713 2020-05-02 Midlands 51
## 714 2020-05-03 Midlands 52
## 715 2020-05-04 Midlands 61
## 716 2020-05-05 Midlands 59
## 717 2020-05-06 Midlands 59
## 718 2020-05-07 Midlands 48
## 719 2020-05-08 Midlands 34
## 720 2020-05-09 Midlands 37
## 721 2020-05-10 Midlands 42
## 722 2020-05-11 Midlands 33
## 723 2020-05-12 Midlands 45
## 724 2020-05-13 Midlands 40
## 725 2020-05-14 Midlands 39
## 726 2020-05-15 Midlands 40
## 727 2020-05-16 Midlands 34
## 728 2020-05-17 Midlands 31
## 729 2020-05-18 Midlands 36
## 730 2020-05-19 Midlands 35
## 731 2020-05-20 Midlands 36
## 732 2020-05-21 Midlands 32
## 733 2020-05-22 Midlands 27
## 734 2020-05-23 Midlands 34
## 735 2020-05-24 Midlands 20
## 736 2020-05-25 Midlands 26
## 737 2020-05-26 Midlands 33
## 738 2020-05-27 Midlands 29
## 739 2020-05-28 Midlands 28
## 740 2020-05-29 Midlands 20
## 741 2020-05-30 Midlands 21
## 742 2020-05-31 Midlands 22
## 743 2020-06-01 Midlands 20
## 744 2020-06-02 Midlands 22
## 745 2020-06-03 Midlands 24
## 746 2020-06-04 Midlands 16
## 747 2020-06-05 Midlands 21
## 748 2020-06-06 Midlands 20
## 749 2020-06-07 Midlands 17
## 750 2020-06-08 Midlands 16
## 751 2020-06-09 Midlands 18
## 752 2020-06-10 Midlands 15
## 753 2020-06-11 Midlands 13
## 754 2020-06-12 Midlands 12
## 755 2020-06-13 Midlands 6
## 756 2020-06-14 Midlands 18
## 757 2020-06-15 Midlands 12
## 758 2020-06-16 Midlands 15
## 759 2020-06-17 Midlands 11
## 760 2020-06-18 Midlands 15
## 761 2020-06-19 Midlands 10
## 762 2020-06-20 Midlands 15
## 763 2020-06-21 Midlands 14
## 764 2020-06-22 Midlands 14
## 765 2020-06-23 Midlands 16
## 766 2020-06-24 Midlands 15
## 767 2020-06-25 Midlands 18
## 768 2020-06-26 Midlands 5
## 769 2020-06-27 Midlands 5
## 770 2020-06-28 Midlands 7
## 771 2020-06-29 Midlands 6
## 772 2020-06-30 Midlands 6
## 773 2020-07-01 Midlands 7
## 774 2020-07-02 Midlands 10
## 775 2020-07-03 Midlands 3
## 776 2020-07-04 Midlands 4
## 777 2020-07-05 Midlands 6
## 778 2020-07-06 Midlands 5
## 779 2020-07-07 Midlands 3
## 780 2020-07-08 Midlands 5
## 781 2020-07-09 Midlands 9
## 782 2020-07-10 Midlands 3
## 783 2020-07-11 Midlands 0
## 784 2020-07-12 Midlands 5
## 785 2020-07-13 Midlands 1
## 786 2020-07-14 Midlands 1
## 787 2020-07-15 Midlands 6
## 788 2020-07-16 Midlands 2
## 789 2020-07-17 Midlands 3
## 790 2020-07-18 Midlands 3
## 791 2020-07-19 Midlands 3
## 792 2020-07-20 Midlands 3
## 793 2020-07-21 Midlands 1
## 794 2020-07-22 Midlands 2
## 795 2020-07-23 Midlands 6
## 796 2020-07-24 Midlands 1
## 797 2020-07-25 Midlands 4
## 798 2020-07-26 Midlands 4
## 799 2020-07-27 Midlands 5
## 800 2020-07-28 Midlands 1
## 801 2020-07-29 Midlands 1
## 802 2020-07-30 Midlands 1
## 803 2020-07-31 Midlands 2
## 804 2020-08-01 Midlands 0
## 805 2020-08-02 Midlands 1
## 806 2020-08-03 Midlands 2
## 807 2020-08-04 Midlands 1
## 808 2020-08-05 Midlands 1
## 809 2020-08-06 Midlands 0
## 810 2020-08-07 Midlands 3
## 811 2020-08-08 Midlands 2
## 812 2020-08-09 Midlands 0
## 813 2020-08-10 Midlands 0
## 814 2020-08-11 Midlands 2
## 815 2020-08-12 Midlands 0
## 816 2020-08-13 Midlands 0
## 817 2020-08-14 Midlands 0
## 818 2020-08-15 Midlands 1
## 819 2020-08-16 Midlands 0
## 820 2020-08-17 Midlands 0
## 821 2020-08-18 Midlands 0
## 822 2020-08-19 Midlands 0
## 823 2020-08-20 Midlands 0
## 824 2020-08-21 Midlands 1
## 825 2020-08-22 Midlands 0
## 826 2020-08-23 Midlands 0
## 827 2020-08-24 Midlands 0
## 828 2020-08-25 Midlands 2
## 829 2020-08-26 Midlands 3
## 830 2020-08-27 Midlands 2
## 831 2020-08-28 Midlands 1
## 832 2020-08-29 Midlands 0
## 833 2020-08-30 Midlands 2
## 834 2020-08-31 Midlands 1
## 835 2020-09-01 Midlands 0
## 836 2020-09-02 Midlands 2
## 837 2020-09-03 Midlands 0
## 838 2020-09-04 Midlands 0
## 839 2020-09-05 Midlands 0
## 840 2020-09-06 Midlands 1
## 841 2020-09-07 Midlands 1
## 842 2020-09-08 Midlands 3
## 843 2020-09-09 Midlands 0
## 844 2020-09-10 Midlands 1
## 845 2020-09-11 Midlands 1
## 846 2020-09-12 Midlands 2
## 847 2020-09-13 Midlands 4
## 848 2020-09-14 Midlands 1
## 849 2020-09-15 Midlands 2
## 850 2020-09-16 Midlands 3
## 851 2020-09-17 Midlands 2
## 852 2020-09-18 Midlands 5
## 853 2020-09-19 Midlands 2
## 854 2020-09-20 Midlands 7
## 855 2020-09-21 Midlands 3
## 856 2020-09-22 Midlands 4
## 857 2020-09-23 Midlands 10
## 858 2020-09-24 Midlands 7
## 859 2020-09-25 Midlands 4
## 860 2020-09-26 Midlands 5
## 861 2020-09-27 Midlands 9
## 862 2020-09-28 Midlands 6
## 863 2020-09-29 Midlands 4
## 864 2020-09-30 Midlands 5
## 865 2020-10-01 Midlands 8
## 866 2020-10-02 Midlands 7
## 867 2020-10-03 Midlands 6
## 868 2020-10-04 Midlands 7
## 869 2020-10-05 Midlands 6
## 870 2020-10-06 Midlands 5
## 871 2020-10-07 Midlands 9
## 872 2020-10-08 Midlands 8
## 873 2020-10-09 Midlands 7
## 874 2020-10-10 Midlands 2
## 875 2020-10-11 Midlands 15
## 876 2020-10-12 Midlands 7
## 877 2020-10-13 Midlands 16
## 878 2020-10-14 Midlands 12
## 879 2020-10-15 Midlands 11
## 880 2020-10-16 Midlands 18
## 881 2020-10-17 Midlands 25
## 882 2020-10-18 Midlands 11
## 883 2020-10-19 Midlands 14
## 884 2020-10-20 Midlands 19
## 885 2020-10-21 Midlands 15
## 886 2020-10-22 Midlands 34
## 887 2020-10-23 Midlands 32
## 888 2020-10-24 Midlands 24
## 889 2020-10-25 Midlands 30
## 890 2020-10-26 Midlands 33
## 891 2020-10-27 Midlands 38
## 892 2020-10-28 Midlands 30
## 893 2020-10-29 Midlands 42
## 894 2020-10-30 Midlands 42
## 895 2020-10-31 Midlands 50
## 896 2020-11-01 Midlands 44
## 897 2020-11-02 Midlands 58
## 898 2020-11-03 Midlands 37
## 899 2020-11-04 Midlands 67
## 900 2020-11-05 Midlands 50
## 901 2020-11-06 Midlands 43
## 902 2020-11-07 Midlands 60
## 903 2020-11-08 Midlands 55
## 904 2020-11-09 Midlands 67
## 905 2020-11-10 Midlands 68
## 906 2020-11-11 Midlands 57
## 907 2020-11-12 Midlands 64
## 908 2020-11-13 Midlands 47
## 909 2020-11-14 Midlands 66
## 910 2020-11-15 Midlands 72
## 911 2020-11-16 Midlands 66
## 912 2020-11-17 Midlands 66
## 913 2020-11-18 Midlands 83
## 914 2020-11-19 Midlands 72
## 915 2020-11-20 Midlands 87
## 916 2020-11-21 Midlands 59
## 917 2020-11-22 Midlands 84
## 918 2020-11-23 Midlands 80
## 919 2020-11-24 Midlands 73
## 920 2020-11-25 Midlands 74
## 921 2020-11-26 Midlands 77
## 922 2020-11-27 Midlands 78
## 923 2020-11-28 Midlands 80
## 924 2020-11-29 Midlands 86
## 925 2020-11-30 Midlands 79
## 926 2020-12-01 Midlands 74
## 927 2020-12-02 Midlands 64
## 928 2020-12-03 Midlands 82
## 929 2020-12-04 Midlands 67
## 930 2020-12-05 Midlands 71
## 931 2020-12-06 Midlands 75
## 932 2020-12-07 Midlands 68
## 933 2020-12-08 Midlands 65
## 934 2020-12-09 Midlands 63
## 935 2020-12-10 Midlands 74
## 936 2020-12-11 Midlands 65
## 937 2020-12-12 Midlands 80
## 938 2020-12-13 Midlands 77
## 939 2020-12-14 Midlands 76
## 940 2020-12-15 Midlands 73
## 941 2020-12-16 Midlands 73
## 942 2020-12-17 Midlands 84
## 943 2020-12-18 Midlands 80
## 944 2020-12-19 Midlands 56
## 945 2020-12-20 Midlands 66
## 946 2020-12-21 Midlands 84
## 947 2020-12-22 Midlands 74
## 948 2020-12-23 Midlands 58
## 949 2020-12-24 Midlands 66
## 950 2020-12-25 Midlands 78
## 951 2020-12-26 Midlands 71
## 952 2020-12-27 Midlands 84
## 953 2020-12-28 Midlands 62
## 954 2020-12-29 Midlands 81
## 955 2020-12-30 Midlands 97
## 956 2020-12-31 Midlands 86
## 957 2021-01-01 Midlands 75
## 958 2021-01-02 Midlands 73
## 959 2021-01-03 Midlands 70
## 960 2021-01-04 Midlands 91
## 961 2021-01-05 Midlands 101
## 962 2021-01-06 Midlands 109
## 963 2021-01-07 Midlands 102
## 964 2021-01-08 Midlands 129
## 965 2021-01-09 Midlands 137
## 966 2021-01-10 Midlands 118
## 967 2021-01-11 Midlands 142
## 968 2021-01-12 Midlands 156
## 969 2021-01-13 Midlands 135
## 970 2021-01-14 Midlands 150
## 971 2021-01-15 Midlands 120
## 972 2021-01-16 Midlands 133
## 973 2021-01-17 Midlands 135
## 974 2021-01-18 Midlands 116
## 975 2021-01-19 Midlands 18
## 976 2020-03-01 North East and Yorkshire 0
## 977 2020-03-02 North East and Yorkshire 0
## 978 2020-03-03 North East and Yorkshire 0
## 979 2020-03-04 North East and Yorkshire 0
## 980 2020-03-05 North East and Yorkshire 0
## 981 2020-03-06 North East and Yorkshire 0
## 982 2020-03-07 North East and Yorkshire 0
## 983 2020-03-08 North East and Yorkshire 0
## 984 2020-03-09 North East and Yorkshire 0
## 985 2020-03-10 North East and Yorkshire 0
## 986 2020-03-11 North East and Yorkshire 0
## 987 2020-03-12 North East and Yorkshire 0
## 988 2020-03-13 North East and Yorkshire 0
## 989 2020-03-14 North East and Yorkshire 0
## 990 2020-03-15 North East and Yorkshire 2
## 991 2020-03-16 North East and Yorkshire 3
## 992 2020-03-17 North East and Yorkshire 1
## 993 2020-03-18 North East and Yorkshire 2
## 994 2020-03-19 North East and Yorkshire 6
## 995 2020-03-20 North East and Yorkshire 5
## 996 2020-03-21 North East and Yorkshire 6
## 997 2020-03-22 North East and Yorkshire 7
## 998 2020-03-23 North East and Yorkshire 9
## 999 2020-03-24 North East and Yorkshire 8
## 1000 2020-03-25 North East and Yorkshire 18
## 1001 2020-03-26 North East and Yorkshire 21
## 1002 2020-03-27 North East and Yorkshire 28
## 1003 2020-03-28 North East and Yorkshire 35
## 1004 2020-03-29 North East and Yorkshire 38
## 1005 2020-03-30 North East and Yorkshire 64
## 1006 2020-03-31 North East and Yorkshire 60
## 1007 2020-04-01 North East and Yorkshire 67
## 1008 2020-04-02 North East and Yorkshire 75
## 1009 2020-04-03 North East and Yorkshire 100
## 1010 2020-04-04 North East and Yorkshire 105
## 1011 2020-04-05 North East and Yorkshire 92
## 1012 2020-04-06 North East and Yorkshire 96
## 1013 2020-04-07 North East and Yorkshire 102
## 1014 2020-04-08 North East and Yorkshire 107
## 1015 2020-04-09 North East and Yorkshire 111
## 1016 2020-04-10 North East and Yorkshire 117
## 1017 2020-04-11 North East and Yorkshire 98
## 1018 2020-04-12 North East and Yorkshire 84
## 1019 2020-04-13 North East and Yorkshire 94
## 1020 2020-04-14 North East and Yorkshire 107
## 1021 2020-04-15 North East and Yorkshire 96
## 1022 2020-04-16 North East and Yorkshire 103
## 1023 2020-04-17 North East and Yorkshire 88
## 1024 2020-04-18 North East and Yorkshire 95
## 1025 2020-04-19 North East and Yorkshire 88
## 1026 2020-04-20 North East and Yorkshire 100
## 1027 2020-04-21 North East and Yorkshire 76
## 1028 2020-04-22 North East and Yorkshire 84
## 1029 2020-04-23 North East and Yorkshire 63
## 1030 2020-04-24 North East and Yorkshire 72
## 1031 2020-04-25 North East and Yorkshire 69
## 1032 2020-04-26 North East and Yorkshire 65
## 1033 2020-04-27 North East and Yorkshire 65
## 1034 2020-04-28 North East and Yorkshire 57
## 1035 2020-04-29 North East and Yorkshire 69
## 1036 2020-04-30 North East and Yorkshire 57
## 1037 2020-05-01 North East and Yorkshire 64
## 1038 2020-05-02 North East and Yorkshire 48
## 1039 2020-05-03 North East and Yorkshire 40
## 1040 2020-05-04 North East and Yorkshire 49
## 1041 2020-05-05 North East and Yorkshire 40
## 1042 2020-05-06 North East and Yorkshire 51
## 1043 2020-05-07 North East and Yorkshire 45
## 1044 2020-05-08 North East and Yorkshire 42
## 1045 2020-05-09 North East and Yorkshire 44
## 1046 2020-05-10 North East and Yorkshire 40
## 1047 2020-05-11 North East and Yorkshire 29
## 1048 2020-05-12 North East and Yorkshire 27
## 1049 2020-05-13 North East and Yorkshire 28
## 1050 2020-05-14 North East and Yorkshire 31
## 1051 2020-05-15 North East and Yorkshire 32
## 1052 2020-05-16 North East and Yorkshire 35
## 1053 2020-05-17 North East and Yorkshire 26
## 1054 2020-05-18 North East and Yorkshire 30
## 1055 2020-05-19 North East and Yorkshire 27
## 1056 2020-05-20 North East and Yorkshire 22
## 1057 2020-05-21 North East and Yorkshire 33
## 1058 2020-05-22 North East and Yorkshire 22
## 1059 2020-05-23 North East and Yorkshire 18
## 1060 2020-05-24 North East and Yorkshire 26
## 1061 2020-05-25 North East and Yorkshire 21
## 1062 2020-05-26 North East and Yorkshire 21
## 1063 2020-05-27 North East and Yorkshire 22
## 1064 2020-05-28 North East and Yorkshire 21
## 1065 2020-05-29 North East and Yorkshire 25
## 1066 2020-05-30 North East and Yorkshire 20
## 1067 2020-05-31 North East and Yorkshire 20
## 1068 2020-06-01 North East and Yorkshire 17
## 1069 2020-06-02 North East and Yorkshire 23
## 1070 2020-06-03 North East and Yorkshire 24
## 1071 2020-06-04 North East and Yorkshire 17
## 1072 2020-06-05 North East and Yorkshire 18
## 1073 2020-06-06 North East and Yorkshire 21
## 1074 2020-06-07 North East and Yorkshire 14
## 1075 2020-06-08 North East and Yorkshire 11
## 1076 2020-06-09 North East and Yorkshire 12
## 1077 2020-06-10 North East and Yorkshire 19
## 1078 2020-06-11 North East and Yorkshire 7
## 1079 2020-06-12 North East and Yorkshire 9
## 1080 2020-06-13 North East and Yorkshire 10
## 1081 2020-06-14 North East and Yorkshire 11
## 1082 2020-06-15 North East and Yorkshire 9
## 1083 2020-06-16 North East and Yorkshire 10
## 1084 2020-06-17 North East and Yorkshire 9
## 1085 2020-06-18 North East and Yorkshire 11
## 1086 2020-06-19 North East and Yorkshire 6
## 1087 2020-06-20 North East and Yorkshire 5
## 1088 2020-06-21 North East and Yorkshire 4
## 1089 2020-06-22 North East and Yorkshire 7
## 1090 2020-06-23 North East and Yorkshire 8
## 1091 2020-06-24 North East and Yorkshire 10
## 1092 2020-06-25 North East and Yorkshire 4
## 1093 2020-06-26 North East and Yorkshire 8
## 1094 2020-06-27 North East and Yorkshire 4
## 1095 2020-06-28 North East and Yorkshire 5
## 1096 2020-06-29 North East and Yorkshire 2
## 1097 2020-06-30 North East and Yorkshire 7
## 1098 2020-07-01 North East and Yorkshire 1
## 1099 2020-07-02 North East and Yorkshire 5
## 1100 2020-07-03 North East and Yorkshire 4
## 1101 2020-07-04 North East and Yorkshire 4
## 1102 2020-07-05 North East and Yorkshire 3
## 1103 2020-07-06 North East and Yorkshire 2
## 1104 2020-07-07 North East and Yorkshire 3
## 1105 2020-07-08 North East and Yorkshire 3
## 1106 2020-07-09 North East and Yorkshire 0
## 1107 2020-07-10 North East and Yorkshire 3
## 1108 2020-07-11 North East and Yorkshire 1
## 1109 2020-07-12 North East and Yorkshire 4
## 1110 2020-07-13 North East and Yorkshire 1
## 1111 2020-07-14 North East and Yorkshire 1
## 1112 2020-07-15 North East and Yorkshire 2
## 1113 2020-07-16 North East and Yorkshire 3
## 1114 2020-07-17 North East and Yorkshire 1
## 1115 2020-07-18 North East and Yorkshire 2
## 1116 2020-07-19 North East and Yorkshire 2
## 1117 2020-07-20 North East and Yorkshire 1
## 1118 2020-07-21 North East and Yorkshire 1
## 1119 2020-07-22 North East and Yorkshire 6
## 1120 2020-07-23 North East and Yorkshire 0
## 1121 2020-07-24 North East and Yorkshire 1
## 1122 2020-07-25 North East and Yorkshire 5
## 1123 2020-07-26 North East and Yorkshire 1
## 1124 2020-07-27 North East and Yorkshire 0
## 1125 2020-07-28 North East and Yorkshire 2
## 1126 2020-07-29 North East and Yorkshire 1
## 1127 2020-07-30 North East and Yorkshire 0
## 1128 2020-07-31 North East and Yorkshire 1
## 1129 2020-08-01 North East and Yorkshire 3
## 1130 2020-08-02 North East and Yorkshire 2
## 1131 2020-08-03 North East and Yorkshire 1
## 1132 2020-08-04 North East and Yorkshire 3
## 1133 2020-08-05 North East and Yorkshire 1
## 1134 2020-08-06 North East and Yorkshire 4
## 1135 2020-08-07 North East and Yorkshire 0
## 1136 2020-08-08 North East and Yorkshire 2
## 1137 2020-08-09 North East and Yorkshire 3
## 1138 2020-08-10 North East and Yorkshire 3
## 1139 2020-08-11 North East and Yorkshire 2
## 1140 2020-08-12 North East and Yorkshire 2
## 1141 2020-08-13 North East and Yorkshire 0
## 1142 2020-08-14 North East and Yorkshire 1
## 1143 2020-08-15 North East and Yorkshire 1
## 1144 2020-08-16 North East and Yorkshire 0
## 1145 2020-08-17 North East and Yorkshire 6
## 1146 2020-08-18 North East and Yorkshire 1
## 1147 2020-08-19 North East and Yorkshire 0
## 1148 2020-08-20 North East and Yorkshire 0
## 1149 2020-08-21 North East and Yorkshire 1
## 1150 2020-08-22 North East and Yorkshire 1
## 1151 2020-08-23 North East and Yorkshire 3
## 1152 2020-08-24 North East and Yorkshire 0
## 1153 2020-08-25 North East and Yorkshire 2
## 1154 2020-08-26 North East and Yorkshire 2
## 1155 2020-08-27 North East and Yorkshire 1
## 1156 2020-08-28 North East and Yorkshire 0
## 1157 2020-08-29 North East and Yorkshire 1
## 1158 2020-08-30 North East and Yorkshire 0
## 1159 2020-08-31 North East and Yorkshire 0
## 1160 2020-09-01 North East and Yorkshire 2
## 1161 2020-09-02 North East and Yorkshire 3
## 1162 2020-09-03 North East and Yorkshire 1
## 1163 2020-09-04 North East and Yorkshire 1
## 1164 2020-09-05 North East and Yorkshire 2
## 1165 2020-09-06 North East and Yorkshire 1
## 1166 2020-09-07 North East and Yorkshire 0
## 1167 2020-09-08 North East and Yorkshire 1
## 1168 2020-09-09 North East and Yorkshire 2
## 1169 2020-09-10 North East and Yorkshire 0
## 1170 2020-09-11 North East and Yorkshire 3
## 1171 2020-09-12 North East and Yorkshire 1
## 1172 2020-09-13 North East and Yorkshire 3
## 1173 2020-09-14 North East and Yorkshire 4
## 1174 2020-09-15 North East and Yorkshire 3
## 1175 2020-09-16 North East and Yorkshire 3
## 1176 2020-09-17 North East and Yorkshire 5
## 1177 2020-09-18 North East and Yorkshire 6
## 1178 2020-09-19 North East and Yorkshire 2
## 1179 2020-09-20 North East and Yorkshire 9
## 1180 2020-09-21 North East and Yorkshire 7
## 1181 2020-09-22 North East and Yorkshire 5
## 1182 2020-09-23 North East and Yorkshire 6
## 1183 2020-09-24 North East and Yorkshire 3
## 1184 2020-09-25 North East and Yorkshire 5
## 1185 2020-09-26 North East and Yorkshire 7
## 1186 2020-09-27 North East and Yorkshire 10
## 1187 2020-09-28 North East and Yorkshire 6
## 1188 2020-09-29 North East and Yorkshire 7
## 1189 2020-09-30 North East and Yorkshire 7
## 1190 2020-10-01 North East and Yorkshire 8
## 1191 2020-10-02 North East and Yorkshire 16
## 1192 2020-10-03 North East and Yorkshire 12
## 1193 2020-10-04 North East and Yorkshire 13
## 1194 2020-10-05 North East and Yorkshire 10
## 1195 2020-10-06 North East and Yorkshire 15
## 1196 2020-10-07 North East and Yorkshire 13
## 1197 2020-10-08 North East and Yorkshire 16
## 1198 2020-10-09 North East and Yorkshire 10
## 1199 2020-10-10 North East and Yorkshire 16
## 1200 2020-10-11 North East and Yorkshire 16
## 1201 2020-10-12 North East and Yorkshire 15
## 1202 2020-10-13 North East and Yorkshire 21
## 1203 2020-10-14 North East and Yorkshire 20
## 1204 2020-10-15 North East and Yorkshire 23
## 1205 2020-10-16 North East and Yorkshire 24
## 1206 2020-10-17 North East and Yorkshire 34
## 1207 2020-10-18 North East and Yorkshire 22
## 1208 2020-10-19 North East and Yorkshire 34
## 1209 2020-10-20 North East and Yorkshire 36
## 1210 2020-10-21 North East and Yorkshire 42
## 1211 2020-10-22 North East and Yorkshire 33
## 1212 2020-10-23 North East and Yorkshire 31
## 1213 2020-10-24 North East and Yorkshire 34
## 1214 2020-10-25 North East and Yorkshire 35
## 1215 2020-10-26 North East and Yorkshire 45
## 1216 2020-10-27 North East and Yorkshire 45
## 1217 2020-10-28 North East and Yorkshire 39
## 1218 2020-10-29 North East and Yorkshire 51
## 1219 2020-10-30 North East and Yorkshire 48
## 1220 2020-10-31 North East and Yorkshire 58
## 1221 2020-11-01 North East and Yorkshire 48
## 1222 2020-11-02 North East and Yorkshire 50
## 1223 2020-11-03 North East and Yorkshire 48
## 1224 2020-11-04 North East and Yorkshire 57
## 1225 2020-11-05 North East and Yorkshire 57
## 1226 2020-11-06 North East and Yorkshire 57
## 1227 2020-11-07 North East and Yorkshire 75
## 1228 2020-11-08 North East and Yorkshire 61
## 1229 2020-11-09 North East and Yorkshire 87
## 1230 2020-11-10 North East and Yorkshire 65
## 1231 2020-11-11 North East and Yorkshire 59
## 1232 2020-11-12 North East and Yorkshire 77
## 1233 2020-11-13 North East and Yorkshire 78
## 1234 2020-11-14 North East and Yorkshire 72
## 1235 2020-11-15 North East and Yorkshire 77
## 1236 2020-11-16 North East and Yorkshire 51
## 1237 2020-11-17 North East and Yorkshire 68
## 1238 2020-11-18 North East and Yorkshire 80
## 1239 2020-11-19 North East and Yorkshire 72
## 1240 2020-11-20 North East and Yorkshire 75
## 1241 2020-11-21 North East and Yorkshire 54
## 1242 2020-11-22 North East and Yorkshire 80
## 1243 2020-11-23 North East and Yorkshire 83
## 1244 2020-11-24 North East and Yorkshire 81
## 1245 2020-11-25 North East and Yorkshire 70
## 1246 2020-11-26 North East and Yorkshire 64
## 1247 2020-11-27 North East and Yorkshire 62
## 1248 2020-11-28 North East and Yorkshire 76
## 1249 2020-11-29 North East and Yorkshire 61
## 1250 2020-11-30 North East and Yorkshire 55
## 1251 2020-12-01 North East and Yorkshire 44
## 1252 2020-12-02 North East and Yorkshire 59
## 1253 2020-12-03 North East and Yorkshire 71
## 1254 2020-12-04 North East and Yorkshire 64
## 1255 2020-12-05 North East and Yorkshire 48
## 1256 2020-12-06 North East and Yorkshire 63
## 1257 2020-12-07 North East and Yorkshire 49
## 1258 2020-12-08 North East and Yorkshire 54
## 1259 2020-12-09 North East and Yorkshire 49
## 1260 2020-12-10 North East and Yorkshire 55
## 1261 2020-12-11 North East and Yorkshire 55
## 1262 2020-12-12 North East and Yorkshire 55
## 1263 2020-12-13 North East and Yorkshire 51
## 1264 2020-12-14 North East and Yorkshire 49
## 1265 2020-12-15 North East and Yorkshire 54
## 1266 2020-12-16 North East and Yorkshire 40
## 1267 2020-12-17 North East and Yorkshire 49
## 1268 2020-12-18 North East and Yorkshire 58
## 1269 2020-12-19 North East and Yorkshire 48
## 1270 2020-12-20 North East and Yorkshire 52
## 1271 2020-12-21 North East and Yorkshire 35
## 1272 2020-12-22 North East and Yorkshire 57
## 1273 2020-12-23 North East and Yorkshire 56
## 1274 2020-12-24 North East and Yorkshire 48
## 1275 2020-12-25 North East and Yorkshire 56
## 1276 2020-12-26 North East and Yorkshire 65
## 1277 2020-12-27 North East and Yorkshire 67
## 1278 2020-12-28 North East and Yorkshire 61
## 1279 2020-12-29 North East and Yorkshire 57
## 1280 2020-12-30 North East and Yorkshire 41
## 1281 2020-12-31 North East and Yorkshire 48
## 1282 2021-01-01 North East and Yorkshire 68
## 1283 2021-01-02 North East and Yorkshire 55
## 1284 2021-01-03 North East and Yorkshire 48
## 1285 2021-01-04 North East and Yorkshire 61
## 1286 2021-01-05 North East and Yorkshire 61
## 1287 2021-01-06 North East and Yorkshire 57
## 1288 2021-01-07 North East and Yorkshire 66
## 1289 2021-01-08 North East and Yorkshire 66
## 1290 2021-01-09 North East and Yorkshire 50
## 1291 2021-01-10 North East and Yorkshire 77
## 1292 2021-01-11 North East and Yorkshire 78
## 1293 2021-01-12 North East and Yorkshire 61
## 1294 2021-01-13 North East and Yorkshire 67
## 1295 2021-01-14 North East and Yorkshire 64
## 1296 2021-01-15 North East and Yorkshire 78
## 1297 2021-01-16 North East and Yorkshire 92
## 1298 2021-01-17 North East and Yorkshire 67
## 1299 2021-01-18 North East and Yorkshire 63
## 1300 2021-01-19 North East and Yorkshire 25
## 1301 2020-03-01 North West 0
## 1302 2020-03-02 North West 0
## 1303 2020-03-03 North West 0
## 1304 2020-03-04 North West 0
## 1305 2020-03-05 North West 1
## 1306 2020-03-06 North West 0
## 1307 2020-03-07 North West 0
## 1308 2020-03-08 North West 1
## 1309 2020-03-09 North West 0
## 1310 2020-03-10 North West 0
## 1311 2020-03-11 North West 0
## 1312 2020-03-12 North West 2
## 1313 2020-03-13 North West 3
## 1314 2020-03-14 North West 1
## 1315 2020-03-15 North West 4
## 1316 2020-03-16 North West 2
## 1317 2020-03-17 North West 4
## 1318 2020-03-18 North West 6
## 1319 2020-03-19 North West 7
## 1320 2020-03-20 North West 10
## 1321 2020-03-21 North West 11
## 1322 2020-03-22 North West 13
## 1323 2020-03-23 North West 15
## 1324 2020-03-24 North West 21
## 1325 2020-03-25 North West 21
## 1326 2020-03-26 North West 29
## 1327 2020-03-27 North West 36
## 1328 2020-03-28 North West 28
## 1329 2020-03-29 North West 46
## 1330 2020-03-30 North West 67
## 1331 2020-03-31 North West 52
## 1332 2020-04-01 North West 86
## 1333 2020-04-02 North West 96
## 1334 2020-04-03 North West 95
## 1335 2020-04-04 North West 98
## 1336 2020-04-05 North West 102
## 1337 2020-04-06 North West 100
## 1338 2020-04-07 North West 136
## 1339 2020-04-08 North West 127
## 1340 2020-04-09 North West 119
## 1341 2020-04-10 North West 117
## 1342 2020-04-11 North West 138
## 1343 2020-04-12 North West 125
## 1344 2020-04-13 North West 130
## 1345 2020-04-14 North West 130
## 1346 2020-04-15 North West 114
## 1347 2020-04-16 North West 135
## 1348 2020-04-17 North West 98
## 1349 2020-04-18 North West 113
## 1350 2020-04-19 North West 71
## 1351 2020-04-20 North West 83
## 1352 2020-04-21 North West 76
## 1353 2020-04-22 North West 86
## 1354 2020-04-23 North West 85
## 1355 2020-04-24 North West 66
## 1356 2020-04-25 North West 66
## 1357 2020-04-26 North West 55
## 1358 2020-04-27 North West 54
## 1359 2020-04-28 North West 57
## 1360 2020-04-29 North West 63
## 1361 2020-04-30 North West 60
## 1362 2020-05-01 North West 45
## 1363 2020-05-02 North West 56
## 1364 2020-05-03 North West 55
## 1365 2020-05-04 North West 48
## 1366 2020-05-05 North West 48
## 1367 2020-05-06 North West 44
## 1368 2020-05-07 North West 49
## 1369 2020-05-08 North West 42
## 1370 2020-05-09 North West 31
## 1371 2020-05-10 North West 42
## 1372 2020-05-11 North West 35
## 1373 2020-05-12 North West 38
## 1374 2020-05-13 North West 25
## 1375 2020-05-14 North West 26
## 1376 2020-05-15 North West 33
## 1377 2020-05-16 North West 32
## 1378 2020-05-17 North West 24
## 1379 2020-05-18 North West 31
## 1380 2020-05-19 North West 35
## 1381 2020-05-20 North West 27
## 1382 2020-05-21 North West 28
## 1383 2020-05-22 North West 26
## 1384 2020-05-23 North West 31
## 1385 2020-05-24 North West 26
## 1386 2020-05-25 North West 31
## 1387 2020-05-26 North West 27
## 1388 2020-05-27 North West 27
## 1389 2020-05-28 North West 28
## 1390 2020-05-29 North West 20
## 1391 2020-05-30 North West 19
## 1392 2020-05-31 North West 13
## 1393 2020-06-01 North West 12
## 1394 2020-06-02 North West 27
## 1395 2020-06-03 North West 22
## 1396 2020-06-04 North West 22
## 1397 2020-06-05 North West 16
## 1398 2020-06-06 North West 26
## 1399 2020-06-07 North West 20
## 1400 2020-06-08 North West 23
## 1401 2020-06-09 North West 17
## 1402 2020-06-10 North West 16
## 1403 2020-06-11 North West 16
## 1404 2020-06-12 North West 11
## 1405 2020-06-13 North West 10
## 1406 2020-06-14 North West 15
## 1407 2020-06-15 North West 16
## 1408 2020-06-16 North West 16
## 1409 2020-06-17 North West 13
## 1410 2020-06-18 North West 14
## 1411 2020-06-19 North West 7
## 1412 2020-06-20 North West 11
## 1413 2020-06-21 North West 8
## 1414 2020-06-22 North West 11
## 1415 2020-06-23 North West 13
## 1416 2020-06-24 North West 13
## 1417 2020-06-25 North West 15
## 1418 2020-06-26 North West 6
## 1419 2020-06-27 North West 7
## 1420 2020-06-28 North West 9
## 1421 2020-06-29 North West 9
## 1422 2020-06-30 North West 7
## 1423 2020-07-01 North West 3
## 1424 2020-07-02 North West 6
## 1425 2020-07-03 North West 7
## 1426 2020-07-04 North West 4
## 1427 2020-07-05 North West 6
## 1428 2020-07-06 North West 9
## 1429 2020-07-07 North West 8
## 1430 2020-07-08 North West 5
## 1431 2020-07-09 North West 10
## 1432 2020-07-10 North West 2
## 1433 2020-07-11 North West 5
## 1434 2020-07-12 North West 0
## 1435 2020-07-13 North West 6
## 1436 2020-07-14 North West 4
## 1437 2020-07-15 North West 5
## 1438 2020-07-16 North West 2
## 1439 2020-07-17 North West 4
## 1440 2020-07-18 North West 5
## 1441 2020-07-19 North West 3
## 1442 2020-07-20 North West 0
## 1443 2020-07-21 North West 2
## 1444 2020-07-22 North West 3
## 1445 2020-07-23 North West 3
## 1446 2020-07-24 North West 1
## 1447 2020-07-25 North West 1
## 1448 2020-07-26 North West 3
## 1449 2020-07-27 North West 1
## 1450 2020-07-28 North West 1
## 1451 2020-07-29 North West 2
## 1452 2020-07-30 North West 2
## 1453 2020-07-31 North West 0
## 1454 2020-08-01 North West 2
## 1455 2020-08-02 North West 1
## 1456 2020-08-03 North West 8
## 1457 2020-08-04 North West 3
## 1458 2020-08-05 North West 2
## 1459 2020-08-06 North West 2
## 1460 2020-08-07 North West 2
## 1461 2020-08-08 North West 2
## 1462 2020-08-09 North West 3
## 1463 2020-08-10 North West 2
## 1464 2020-08-11 North West 3
## 1465 2020-08-12 North West 0
## 1466 2020-08-13 North West 2
## 1467 2020-08-14 North West 2
## 1468 2020-08-15 North West 6
## 1469 2020-08-16 North West 2
## 1470 2020-08-17 North West 1
## 1471 2020-08-18 North West 2
## 1472 2020-08-19 North West 1
## 1473 2020-08-20 North West 1
## 1474 2020-08-21 North West 4
## 1475 2020-08-22 North West 3
## 1476 2020-08-23 North West 5
## 1477 2020-08-24 North West 4
## 1478 2020-08-25 North West 3
## 1479 2020-08-26 North West 4
## 1480 2020-08-27 North West 1
## 1481 2020-08-28 North West 2
## 1482 2020-08-29 North West 0
## 1483 2020-08-30 North West 2
## 1484 2020-08-31 North West 3
## 1485 2020-09-01 North West 0
## 1486 2020-09-02 North West 2
## 1487 2020-09-03 North West 1
## 1488 2020-09-04 North West 3
## 1489 2020-09-05 North West 6
## 1490 2020-09-06 North West 1
## 1491 2020-09-07 North West 8
## 1492 2020-09-08 North West 6
## 1493 2020-09-09 North West 5
## 1494 2020-09-10 North West 5
## 1495 2020-09-11 North West 1
## 1496 2020-09-12 North West 4
## 1497 2020-09-13 North West 2
## 1498 2020-09-14 North West 4
## 1499 2020-09-15 North West 4
## 1500 2020-09-16 North West 6
## 1501 2020-09-17 North West 7
## 1502 2020-09-18 North West 6
## 1503 2020-09-19 North West 3
## 1504 2020-09-20 North West 2
## 1505 2020-09-21 North West 2
## 1506 2020-09-22 North West 9
## 1507 2020-09-23 North West 14
## 1508 2020-09-24 North West 10
## 1509 2020-09-25 North West 8
## 1510 2020-09-26 North West 14
## 1511 2020-09-27 North West 11
## 1512 2020-09-28 North West 15
## 1513 2020-09-29 North West 12
## 1514 2020-09-30 North West 17
## 1515 2020-10-01 North West 17
## 1516 2020-10-02 North West 20
## 1517 2020-10-03 North West 15
## 1518 2020-10-04 North West 15
## 1519 2020-10-05 North West 15
## 1520 2020-10-06 North West 20
## 1521 2020-10-07 North West 20
## 1522 2020-10-08 North West 22
## 1523 2020-10-09 North West 23
## 1524 2020-10-10 North West 31
## 1525 2020-10-11 North West 31
## 1526 2020-10-12 North West 35
## 1527 2020-10-13 North West 26
## 1528 2020-10-14 North West 35
## 1529 2020-10-15 North West 36
## 1530 2020-10-16 North West 34
## 1531 2020-10-17 North West 52
## 1532 2020-10-18 North West 40
## 1533 2020-10-19 North West 43
## 1534 2020-10-20 North West 48
## 1535 2020-10-21 North West 51
## 1536 2020-10-22 North West 49
## 1537 2020-10-23 North West 50
## 1538 2020-10-24 North West 51
## 1539 2020-10-25 North West 63
## 1540 2020-10-26 North West 53
## 1541 2020-10-27 North West 49
## 1542 2020-10-28 North West 57
## 1543 2020-10-29 North West 74
## 1544 2020-10-30 North West 73
## 1545 2020-10-31 North West 63
## 1546 2020-11-01 North West 76
## 1547 2020-11-02 North West 65
## 1548 2020-11-03 North West 76
## 1549 2020-11-04 North West 64
## 1550 2020-11-05 North West 67
## 1551 2020-11-06 North West 75
## 1552 2020-11-07 North West 79
## 1553 2020-11-08 North West 83
## 1554 2020-11-09 North West 82
## 1555 2020-11-10 North West 68
## 1556 2020-11-11 North West 61
## 1557 2020-11-12 North West 64
## 1558 2020-11-13 North West 81
## 1559 2020-11-14 North West 61
## 1560 2020-11-15 North West 75
## 1561 2020-11-16 North West 74
## 1562 2020-11-17 North West 73
## 1563 2020-11-18 North West 70
## 1564 2020-11-19 North West 67
## 1565 2020-11-20 North West 52
## 1566 2020-11-21 North West 68
## 1567 2020-11-22 North West 52
## 1568 2020-11-23 North West 54
## 1569 2020-11-24 North West 64
## 1570 2020-11-25 North West 65
## 1571 2020-11-26 North West 53
## 1572 2020-11-27 North West 51
## 1573 2020-11-28 North West 46
## 1574 2020-11-29 North West 54
## 1575 2020-11-30 North West 48
## 1576 2020-12-01 North West 53
## 1577 2020-12-02 North West 48
## 1578 2020-12-03 North West 46
## 1579 2020-12-04 North West 47
## 1580 2020-12-05 North West 37
## 1581 2020-12-06 North West 43
## 1582 2020-12-07 North West 50
## 1583 2020-12-08 North West 49
## 1584 2020-12-09 North West 48
## 1585 2020-12-10 North West 49
## 1586 2020-12-11 North West 41
## 1587 2020-12-12 North West 48
## 1588 2020-12-13 North West 40
## 1589 2020-12-14 North West 51
## 1590 2020-12-15 North West 34
## 1591 2020-12-16 North West 40
## 1592 2020-12-17 North West 25
## 1593 2020-12-18 North West 47
## 1594 2020-12-19 North West 45
## 1595 2020-12-20 North West 37
## 1596 2020-12-21 North West 51
## 1597 2020-12-22 North West 52
## 1598 2020-12-23 North West 50
## 1599 2020-12-24 North West 58
## 1600 2020-12-25 North West 52
## 1601 2020-12-26 North West 56
## 1602 2020-12-27 North West 50
## 1603 2020-12-28 North West 48
## 1604 2020-12-29 North West 48
## 1605 2020-12-30 North West 49
## 1606 2020-12-31 North West 62
## 1607 2021-01-01 North West 54
## 1608 2021-01-02 North West 54
## 1609 2021-01-03 North West 55
## 1610 2021-01-04 North West 50
## 1611 2021-01-05 North West 54
## 1612 2021-01-06 North West 66
## 1613 2021-01-07 North West 60
## 1614 2021-01-08 North West 63
## 1615 2021-01-09 North West 64
## 1616 2021-01-10 North West 62
## 1617 2021-01-11 North West 75
## 1618 2021-01-12 North West 56
## 1619 2021-01-13 North West 77
## 1620 2021-01-14 North West 81
## 1621 2021-01-15 North West 72
## 1622 2021-01-16 North West 72
## 1623 2021-01-17 North West 63
## 1624 2021-01-18 North West 64
## 1625 2021-01-19 North West 23
## 1626 2020-03-01 South East 0
## 1627 2020-03-02 South East 0
## 1628 2020-03-03 South East 1
## 1629 2020-03-04 South East 0
## 1630 2020-03-05 South East 1
## 1631 2020-03-06 South East 0
## 1632 2020-03-07 South East 0
## 1633 2020-03-08 South East 1
## 1634 2020-03-09 South East 1
## 1635 2020-03-10 South East 1
## 1636 2020-03-11 South East 1
## 1637 2020-03-12 South East 0
## 1638 2020-03-13 South East 1
## 1639 2020-03-14 South East 1
## 1640 2020-03-15 South East 5
## 1641 2020-03-16 South East 8
## 1642 2020-03-17 South East 7
## 1643 2020-03-18 South East 10
## 1644 2020-03-19 South East 9
## 1645 2020-03-20 South East 13
## 1646 2020-03-21 South East 7
## 1647 2020-03-22 South East 25
## 1648 2020-03-23 South East 20
## 1649 2020-03-24 South East 22
## 1650 2020-03-25 South East 29
## 1651 2020-03-26 South East 35
## 1652 2020-03-27 South East 36
## 1653 2020-03-28 South East 36
## 1654 2020-03-29 South East 55
## 1655 2020-03-30 South East 58
## 1656 2020-03-31 South East 65
## 1657 2020-04-01 South East 66
## 1658 2020-04-02 South East 55
## 1659 2020-04-03 South East 72
## 1660 2020-04-04 South East 80
## 1661 2020-04-05 South East 82
## 1662 2020-04-06 South East 88
## 1663 2020-04-07 South East 100
## 1664 2020-04-08 South East 83
## 1665 2020-04-09 South East 104
## 1666 2020-04-10 South East 88
## 1667 2020-04-11 South East 88
## 1668 2020-04-12 South East 88
## 1669 2020-04-13 South East 84
## 1670 2020-04-14 South East 65
## 1671 2020-04-15 South East 72
## 1672 2020-04-16 South East 56
## 1673 2020-04-17 South East 86
## 1674 2020-04-18 South East 57
## 1675 2020-04-19 South East 70
## 1676 2020-04-20 South East 87
## 1677 2020-04-21 South East 51
## 1678 2020-04-22 South East 54
## 1679 2020-04-23 South East 57
## 1680 2020-04-24 South East 64
## 1681 2020-04-25 South East 51
## 1682 2020-04-26 South East 51
## 1683 2020-04-27 South East 41
## 1684 2020-04-28 South East 40
## 1685 2020-04-29 South East 47
## 1686 2020-04-30 South East 29
## 1687 2020-05-01 South East 37
## 1688 2020-05-02 South East 36
## 1689 2020-05-03 South East 17
## 1690 2020-05-04 South East 35
## 1691 2020-05-05 South East 29
## 1692 2020-05-06 South East 25
## 1693 2020-05-07 South East 27
## 1694 2020-05-08 South East 26
## 1695 2020-05-09 South East 28
## 1696 2020-05-10 South East 19
## 1697 2020-05-11 South East 25
## 1698 2020-05-12 South East 27
## 1699 2020-05-13 South East 18
## 1700 2020-05-14 South East 32
## 1701 2020-05-15 South East 25
## 1702 2020-05-16 South East 22
## 1703 2020-05-17 South East 18
## 1704 2020-05-18 South East 22
## 1705 2020-05-19 South East 12
## 1706 2020-05-20 South East 22
## 1707 2020-05-21 South East 15
## 1708 2020-05-22 South East 17
## 1709 2020-05-23 South East 21
## 1710 2020-05-24 South East 17
## 1711 2020-05-25 South East 13
## 1712 2020-05-26 South East 19
## 1713 2020-05-27 South East 19
## 1714 2020-05-28 South East 12
## 1715 2020-05-29 South East 22
## 1716 2020-05-30 South East 8
## 1717 2020-05-31 South East 12
## 1718 2020-06-01 South East 11
## 1719 2020-06-02 South East 13
## 1720 2020-06-03 South East 18
## 1721 2020-06-04 South East 11
## 1722 2020-06-05 South East 11
## 1723 2020-06-06 South East 10
## 1724 2020-06-07 South East 12
## 1725 2020-06-08 South East 8
## 1726 2020-06-09 South East 10
## 1727 2020-06-10 South East 11
## 1728 2020-06-11 South East 5
## 1729 2020-06-12 South East 6
## 1730 2020-06-13 South East 7
## 1731 2020-06-14 South East 7
## 1732 2020-06-15 South East 8
## 1733 2020-06-16 South East 14
## 1734 2020-06-17 South East 9
## 1735 2020-06-18 South East 4
## 1736 2020-06-19 South East 7
## 1737 2020-06-20 South East 5
## 1738 2020-06-21 South East 3
## 1739 2020-06-22 South East 2
## 1740 2020-06-23 South East 9
## 1741 2020-06-24 South East 7
## 1742 2020-06-25 South East 5
## 1743 2020-06-26 South East 8
## 1744 2020-06-27 South East 9
## 1745 2020-06-28 South East 6
## 1746 2020-06-29 South East 5
## 1747 2020-06-30 South East 5
## 1748 2020-07-01 South East 2
## 1749 2020-07-02 South East 8
## 1750 2020-07-03 South East 3
## 1751 2020-07-04 South East 6
## 1752 2020-07-05 South East 5
## 1753 2020-07-06 South East 4
## 1754 2020-07-07 South East 6
## 1755 2020-07-08 South East 3
## 1756 2020-07-09 South East 7
## 1757 2020-07-10 South East 3
## 1758 2020-07-11 South East 4
## 1759 2020-07-12 South East 5
## 1760 2020-07-13 South East 5
## 1761 2020-07-14 South East 5
## 1762 2020-07-15 South East 6
## 1763 2020-07-16 South East 3
## 1764 2020-07-17 South East 1
## 1765 2020-07-18 South East 5
## 1766 2020-07-19 South East 2
## 1767 2020-07-20 South East 6
## 1768 2020-07-21 South East 4
## 1769 2020-07-22 South East 2
## 1770 2020-07-23 South East 3
## 1771 2020-07-24 South East 1
## 1772 2020-07-25 South East 1
## 1773 2020-07-26 South East 3
## 1774 2020-07-27 South East 1
## 1775 2020-07-28 South East 3
## 1776 2020-07-29 South East 2
## 1777 2020-07-30 South East 3
## 1778 2020-07-31 South East 1
## 1779 2020-08-01 South East 2
## 1780 2020-08-02 South East 4
## 1781 2020-08-03 South East 0
## 1782 2020-08-04 South East 0
## 1783 2020-08-05 South East 0
## 1784 2020-08-06 South East 2
## 1785 2020-08-07 South East 0
## 1786 2020-08-08 South East 2
## 1787 2020-08-09 South East 0
## 1788 2020-08-10 South East 2
## 1789 2020-08-11 South East 1
## 1790 2020-08-12 South East 1
## 1791 2020-08-13 South East 0
## 1792 2020-08-14 South East 0
## 1793 2020-08-15 South East 2
## 1794 2020-08-16 South East 1
## 1795 2020-08-17 South East 0
## 1796 2020-08-18 South East 2
## 1797 2020-08-19 South East 1
## 1798 2020-08-20 South East 0
## 1799 2020-08-21 South East 0
## 1800 2020-08-22 South East 0
## 1801 2020-08-23 South East 1
## 1802 2020-08-24 South East 0
## 1803 2020-08-25 South East 1
## 1804 2020-08-26 South East 0
## 1805 2020-08-27 South East 1
## 1806 2020-08-28 South East 2
## 1807 2020-08-29 South East 1
## 1808 2020-08-30 South East 0
## 1809 2020-08-31 South East 2
## 1810 2020-09-01 South East 1
## 1811 2020-09-02 South East 1
## 1812 2020-09-03 South East 0
## 1813 2020-09-04 South East 1
## 1814 2020-09-05 South East 0
## 1815 2020-09-06 South East 1
## 1816 2020-09-07 South East 0
## 1817 2020-09-08 South East 0
## 1818 2020-09-09 South East 0
## 1819 2020-09-10 South East 1
## 1820 2020-09-11 South East 1
## 1821 2020-09-12 South East 0
## 1822 2020-09-13 South East 3
## 1823 2020-09-14 South East 1
## 1824 2020-09-15 South East 2
## 1825 2020-09-16 South East 2
## 1826 2020-09-17 South East 3
## 1827 2020-09-18 South East 1
## 1828 2020-09-19 South East 1
## 1829 2020-09-20 South East 0
## 1830 2020-09-21 South East 3
## 1831 2020-09-22 South East 0
## 1832 2020-09-23 South East 2
## 1833 2020-09-24 South East 1
## 1834 2020-09-25 South East 3
## 1835 2020-09-26 South East 2
## 1836 2020-09-27 South East 2
## 1837 2020-09-28 South East 6
## 1838 2020-09-29 South East 3
## 1839 2020-09-30 South East 4
## 1840 2020-10-01 South East 4
## 1841 2020-10-02 South East 2
## 1842 2020-10-03 South East 1
## 1843 2020-10-04 South East 1
## 1844 2020-10-05 South East 2
## 1845 2020-10-06 South East 1
## 1846 2020-10-07 South East 4
## 1847 2020-10-08 South East 1
## 1848 2020-10-09 South East 1
## 1849 2020-10-10 South East 3
## 1850 2020-10-11 South East 3
## 1851 2020-10-12 South East 4
## 1852 2020-10-13 South East 2
## 1853 2020-10-14 South East 2
## 1854 2020-10-15 South East 3
## 1855 2020-10-16 South East 2
## 1856 2020-10-17 South East 3
## 1857 2020-10-18 South East 4
## 1858 2020-10-19 South East 7
## 1859 2020-10-20 South East 8
## 1860 2020-10-21 South East 9
## 1861 2020-10-22 South East 5
## 1862 2020-10-23 South East 7
## 1863 2020-10-24 South East 5
## 1864 2020-10-25 South East 9
## 1865 2020-10-26 South East 13
## 1866 2020-10-27 South East 10
## 1867 2020-10-28 South East 10
## 1868 2020-10-29 South East 7
## 1869 2020-10-30 South East 6
## 1870 2020-10-31 South East 15
## 1871 2020-11-01 South East 18
## 1872 2020-11-02 South East 13
## 1873 2020-11-03 South East 16
## 1874 2020-11-04 South East 10
## 1875 2020-11-05 South East 10
## 1876 2020-11-06 South East 16
## 1877 2020-11-07 South East 17
## 1878 2020-11-08 South East 18
## 1879 2020-11-09 South East 19
## 1880 2020-11-10 South East 20
## 1881 2020-11-11 South East 19
## 1882 2020-11-12 South East 20
## 1883 2020-11-13 South East 12
## 1884 2020-11-14 South East 24
## 1885 2020-11-15 South East 25
## 1886 2020-11-16 South East 22
## 1887 2020-11-17 South East 23
## 1888 2020-11-18 South East 26
## 1889 2020-11-19 South East 21
## 1890 2020-11-20 South East 18
## 1891 2020-11-21 South East 23
## 1892 2020-11-22 South East 30
## 1893 2020-11-23 South East 29
## 1894 2020-11-24 South East 26
## 1895 2020-11-25 South East 42
## 1896 2020-11-26 South East 30
## 1897 2020-11-27 South East 31
## 1898 2020-11-28 South East 24
## 1899 2020-11-29 South East 37
## 1900 2020-11-30 South East 23
## 1901 2020-12-01 South East 29
## 1902 2020-12-02 South East 33
## 1903 2020-12-03 South East 36
## 1904 2020-12-04 South East 41
## 1905 2020-12-05 South East 36
## 1906 2020-12-06 South East 32
## 1907 2020-12-07 South East 25
## 1908 2020-12-08 South East 43
## 1909 2020-12-09 South East 44
## 1910 2020-12-10 South East 38
## 1911 2020-12-11 South East 48
## 1912 2020-12-12 South East 39
## 1913 2020-12-13 South East 40
## 1914 2020-12-14 South East 38
## 1915 2020-12-15 South East 50
## 1916 2020-12-16 South East 44
## 1917 2020-12-17 South East 51
## 1918 2020-12-18 South East 47
## 1919 2020-12-19 South East 41
## 1920 2020-12-20 South East 55
## 1921 2020-12-21 South East 65
## 1922 2020-12-22 South East 58
## 1923 2020-12-23 South East 69
## 1924 2020-12-24 South East 54
## 1925 2020-12-25 South East 67
## 1926 2020-12-26 South East 72
## 1927 2020-12-27 South East 74
## 1928 2020-12-28 South East 81
## 1929 2020-12-29 South East 77
## 1930 2020-12-30 South East 87
## 1931 2020-12-31 South East 88
## 1932 2021-01-01 South East 59
## 1933 2021-01-02 South East 92
## 1934 2021-01-03 South East 75
## 1935 2021-01-04 South East 100
## 1936 2021-01-05 South East 103
## 1937 2021-01-06 South East 116
## 1938 2021-01-07 South East 113
## 1939 2021-01-08 South East 123
## 1940 2021-01-09 South East 110
## 1941 2021-01-10 South East 120
## 1942 2021-01-11 South East 127
## 1943 2021-01-12 South East 163
## 1944 2021-01-13 South East 126
## 1945 2021-01-14 South East 128
## 1946 2021-01-15 South East 121
## 1947 2021-01-16 South East 133
## 1948 2021-01-17 South East 131
## 1949 2021-01-18 South East 79
## 1950 2021-01-19 South East 16
## 1951 2020-03-01 South West 0
## 1952 2020-03-02 South West 0
## 1953 2020-03-03 South West 0
## 1954 2020-03-04 South West 0
## 1955 2020-03-05 South West 0
## 1956 2020-03-06 South West 0
## 1957 2020-03-07 South West 0
## 1958 2020-03-08 South West 0
## 1959 2020-03-09 South West 0
## 1960 2020-03-10 South West 0
## 1961 2020-03-11 South West 1
## 1962 2020-03-12 South West 0
## 1963 2020-03-13 South West 0
## 1964 2020-03-14 South West 1
## 1965 2020-03-15 South West 0
## 1966 2020-03-16 South West 0
## 1967 2020-03-17 South West 2
## 1968 2020-03-18 South West 2
## 1969 2020-03-19 South West 4
## 1970 2020-03-20 South West 3
## 1971 2020-03-21 South West 6
## 1972 2020-03-22 South West 7
## 1973 2020-03-23 South West 8
## 1974 2020-03-24 South West 7
## 1975 2020-03-25 South West 9
## 1976 2020-03-26 South West 11
## 1977 2020-03-27 South West 13
## 1978 2020-03-28 South West 21
## 1979 2020-03-29 South West 18
## 1980 2020-03-30 South West 23
## 1981 2020-03-31 South West 23
## 1982 2020-04-01 South West 21
## 1983 2020-04-02 South West 23
## 1984 2020-04-03 South West 30
## 1985 2020-04-04 South West 42
## 1986 2020-04-05 South West 32
## 1987 2020-04-06 South West 34
## 1988 2020-04-07 South West 39
## 1989 2020-04-08 South West 47
## 1990 2020-04-09 South West 24
## 1991 2020-04-10 South West 46
## 1992 2020-04-11 South West 43
## 1993 2020-04-12 South West 23
## 1994 2020-04-13 South West 27
## 1995 2020-04-14 South West 24
## 1996 2020-04-15 South West 32
## 1997 2020-04-16 South West 29
## 1998 2020-04-17 South West 33
## 1999 2020-04-18 South West 25
## 2000 2020-04-19 South West 31
## 2001 2020-04-20 South West 26
## 2002 2020-04-21 South West 26
## 2003 2020-04-22 South West 23
## 2004 2020-04-23 South West 17
## 2005 2020-04-24 South West 19
## 2006 2020-04-25 South West 15
## 2007 2020-04-26 South West 27
## 2008 2020-04-27 South West 13
## 2009 2020-04-28 South West 17
## 2010 2020-04-29 South West 15
## 2011 2020-04-30 South West 26
## 2012 2020-05-01 South West 6
## 2013 2020-05-02 South West 7
## 2014 2020-05-03 South West 10
## 2015 2020-05-04 South West 17
## 2016 2020-05-05 South West 14
## 2017 2020-05-06 South West 19
## 2018 2020-05-07 South West 16
## 2019 2020-05-08 South West 6
## 2020 2020-05-09 South West 11
## 2021 2020-05-10 South West 5
## 2022 2020-05-11 South West 8
## 2023 2020-05-12 South West 7
## 2024 2020-05-13 South West 7
## 2025 2020-05-14 South West 6
## 2026 2020-05-15 South West 4
## 2027 2020-05-16 South West 4
## 2028 2020-05-17 South West 6
## 2029 2020-05-18 South West 4
## 2030 2020-05-19 South West 6
## 2031 2020-05-20 South West 1
## 2032 2020-05-21 South West 9
## 2033 2020-05-22 South West 7
## 2034 2020-05-23 South West 6
## 2035 2020-05-24 South West 3
## 2036 2020-05-25 South West 8
## 2037 2020-05-26 South West 11
## 2038 2020-05-27 South West 5
## 2039 2020-05-28 South West 10
## 2040 2020-05-29 South West 7
## 2041 2020-05-30 South West 3
## 2042 2020-05-31 South West 2
## 2043 2020-06-01 South West 7
## 2044 2020-06-02 South West 2
## 2045 2020-06-03 South West 7
## 2046 2020-06-04 South West 2
## 2047 2020-06-05 South West 2
## 2048 2020-06-06 South West 1
## 2049 2020-06-07 South West 3
## 2050 2020-06-08 South West 3
## 2051 2020-06-09 South West 0
## 2052 2020-06-10 South West 1
## 2053 2020-06-11 South West 2
## 2054 2020-06-12 South West 2
## 2055 2020-06-13 South West 2
## 2056 2020-06-14 South West 0
## 2057 2020-06-15 South West 2
## 2058 2020-06-16 South West 2
## 2059 2020-06-17 South West 0
## 2060 2020-06-18 South West 0
## 2061 2020-06-19 South West 0
## 2062 2020-06-20 South West 2
## 2063 2020-06-21 South West 0
## 2064 2020-06-22 South West 1
## 2065 2020-06-23 South West 1
## 2066 2020-06-24 South West 1
## 2067 2020-06-25 South West 0
## 2068 2020-06-26 South West 3
## 2069 2020-06-27 South West 0
## 2070 2020-06-28 South West 0
## 2071 2020-06-29 South West 1
## 2072 2020-06-30 South West 0
## 2073 2020-07-01 South West 0
## 2074 2020-07-02 South West 0
## 2075 2020-07-03 South West 0
## 2076 2020-07-04 South West 0
## 2077 2020-07-05 South West 1
## 2078 2020-07-06 South West 0
## 2079 2020-07-07 South West 0
## 2080 2020-07-08 South West 2
## 2081 2020-07-09 South West 0
## 2082 2020-07-10 South West 1
## 2083 2020-07-11 South West 0
## 2084 2020-07-12 South West 0
## 2085 2020-07-13 South West 1
## 2086 2020-07-14 South West 0
## 2087 2020-07-15 South West 0
## 2088 2020-07-16 South West 0
## 2089 2020-07-17 South West 1
## 2090 2020-07-18 South West 0
## 2091 2020-07-19 South West 0
## 2092 2020-07-20 South West 0
## 2093 2020-07-21 South West 0
## 2094 2020-07-22 South West 0
## 2095 2020-07-23 South West 0
## 2096 2020-07-24 South West 0
## 2097 2020-07-25 South West 0
## 2098 2020-07-26 South West 0
## 2099 2020-07-27 South West 0
## 2100 2020-07-28 South West 0
## 2101 2020-07-29 South West 0
## 2102 2020-07-30 South West 1
## 2103 2020-07-31 South West 0
## 2104 2020-08-01 South West 0
## 2105 2020-08-02 South West 0
## 2106 2020-08-03 South West 0
## 2107 2020-08-04 South West 0
## 2108 2020-08-05 South West 0
## 2109 2020-08-06 South West 0
## 2110 2020-08-07 South West 0
## 2111 2020-08-08 South West 0
## 2112 2020-08-09 South West 0
## 2113 2020-08-10 South West 0
## 2114 2020-08-11 South West 0
## 2115 2020-08-12 South West 0
## 2116 2020-08-13 South West 0
## 2117 2020-08-14 South West 1
## 2118 2020-08-15 South West 0
## 2119 2020-08-16 South West 0
## 2120 2020-08-17 South West 2
## 2121 2020-08-18 South West 0
## 2122 2020-08-19 South West 0
## 2123 2020-08-20 South West 0
## 2124 2020-08-21 South West 0
## 2125 2020-08-22 South West 0
## 2126 2020-08-23 South West 0
## 2127 2020-08-24 South West 0
## 2128 2020-08-25 South West 1
## 2129 2020-08-26 South West 0
## 2130 2020-08-27 South West 1
## 2131 2020-08-28 South West 0
## 2132 2020-08-29 South West 0
## 2133 2020-08-30 South West 0
## 2134 2020-08-31 South West 0
## 2135 2020-09-01 South West 0
## 2136 2020-09-02 South West 0
## 2137 2020-09-03 South West 0
## 2138 2020-09-04 South West 0
## 2139 2020-09-05 South West 0
## 2140 2020-09-06 South West 0
## 2141 2020-09-07 South West 0
## 2142 2020-09-08 South West 1
## 2143 2020-09-09 South West 0
## 2144 2020-09-10 South West 0
## 2145 2020-09-11 South West 0
## 2146 2020-09-12 South West 0
## 2147 2020-09-13 South West 1
## 2148 2020-09-14 South West 0
## 2149 2020-09-15 South West 0
## 2150 2020-09-16 South West 0
## 2151 2020-09-17 South West 1
## 2152 2020-09-18 South West 0
## 2153 2020-09-19 South West 0
## 2154 2020-09-20 South West 1
## 2155 2020-09-21 South West 0
## 2156 2020-09-22 South West 0
## 2157 2020-09-23 South West 0
## 2158 2020-09-24 South West 1
## 2159 2020-09-25 South West 0
## 2160 2020-09-26 South West 0
## 2161 2020-09-27 South West 0
## 2162 2020-09-28 South West 0
## 2163 2020-09-29 South West 0
## 2164 2020-09-30 South West 0
## 2165 2020-10-01 South West 0
## 2166 2020-10-02 South West 1
## 2167 2020-10-03 South West 0
## 2168 2020-10-04 South West 0
## 2169 2020-10-05 South West 0
## 2170 2020-10-06 South West 1
## 2171 2020-10-07 South West 0
## 2172 2020-10-08 South West 1
## 2173 2020-10-09 South West 1
## 2174 2020-10-10 South West 0
## 2175 2020-10-11 South West 4
## 2176 2020-10-12 South West 2
## 2177 2020-10-13 South West 0
## 2178 2020-10-14 South West 3
## 2179 2020-10-15 South West 1
## 2180 2020-10-16 South West 2
## 2181 2020-10-17 South West 8
## 2182 2020-10-18 South West 2
## 2183 2020-10-19 South West 2
## 2184 2020-10-20 South West 3
## 2185 2020-10-21 South West 6
## 2186 2020-10-22 South West 6
## 2187 2020-10-23 South West 5
## 2188 2020-10-24 South West 5
## 2189 2020-10-25 South West 5
## 2190 2020-10-26 South West 7
## 2191 2020-10-27 South West 6
## 2192 2020-10-28 South West 8
## 2193 2020-10-29 South West 11
## 2194 2020-10-30 South West 8
## 2195 2020-10-31 South West 4
## 2196 2020-11-01 South West 5
## 2197 2020-11-02 South West 11
## 2198 2020-11-03 South West 7
## 2199 2020-11-04 South West 8
## 2200 2020-11-05 South West 5
## 2201 2020-11-06 South West 11
## 2202 2020-11-07 South West 10
## 2203 2020-11-08 South West 10
## 2204 2020-11-09 South West 12
## 2205 2020-11-10 South West 6
## 2206 2020-11-11 South West 13
## 2207 2020-11-12 South West 17
## 2208 2020-11-13 South West 9
## 2209 2020-11-14 South West 8
## 2210 2020-11-15 South West 16
## 2211 2020-11-16 South West 18
## 2212 2020-11-17 South West 17
## 2213 2020-11-18 South West 26
## 2214 2020-11-19 South West 15
## 2215 2020-11-20 South West 25
## 2216 2020-11-21 South West 25
## 2217 2020-11-22 South West 23
## 2218 2020-11-23 South West 14
## 2219 2020-11-24 South West 20
## 2220 2020-11-25 South West 25
## 2221 2020-11-26 South West 16
## 2222 2020-11-27 South West 21
## 2223 2020-11-28 South West 35
## 2224 2020-11-29 South West 15
## 2225 2020-11-30 South West 21
## 2226 2020-12-01 South West 19
## 2227 2020-12-02 South West 15
## 2228 2020-12-03 South West 14
## 2229 2020-12-04 South West 20
## 2230 2020-12-05 South West 17
## 2231 2020-12-06 South West 13
## 2232 2020-12-07 South West 14
## 2233 2020-12-08 South West 19
## 2234 2020-12-09 South West 21
## 2235 2020-12-10 South West 20
## 2236 2020-12-11 South West 20
## 2237 2020-12-12 South West 15
## 2238 2020-12-13 South West 19
## 2239 2020-12-14 South West 20
## 2240 2020-12-15 South West 19
## 2241 2020-12-16 South West 9
## 2242 2020-12-17 South West 25
## 2243 2020-12-18 South West 10
## 2244 2020-12-19 South West 22
## 2245 2020-12-20 South West 19
## 2246 2020-12-21 South West 21
## 2247 2020-12-22 South West 10
## 2248 2020-12-23 South West 16
## 2249 2020-12-24 South West 18
## 2250 2020-12-25 South West 19
## 2251 2020-12-26 South West 24
## 2252 2020-12-27 South West 24
## 2253 2020-12-28 South West 20
## 2254 2020-12-29 South West 20
## 2255 2020-12-30 South West 14
## 2256 2020-12-31 South West 26
## 2257 2021-01-01 South West 28
## 2258 2021-01-02 South West 22
## 2259 2021-01-03 South West 24
## 2260 2021-01-04 South West 30
## 2261 2021-01-05 South West 30
## 2262 2021-01-06 South West 24
## 2263 2021-01-07 South West 29
## 2264 2021-01-08 South West 33
## 2265 2021-01-09 South West 26
## 2266 2021-01-10 South West 30
## 2267 2021-01-11 South West 35
## 2268 2021-01-12 South West 46
## 2269 2021-01-13 South West 40
## 2270 2021-01-14 South West 30
## 2271 2021-01-15 South West 36
## 2272 2021-01-16 South West 49
## 2273 2021-01-17 South West 29
## 2274 2021-01-18 South West 26
## 2275 2021-01-19 South West 61.5 Completion date
We extract the completion date from the NHS Pathways file timestamp:
The completion date of the NHS Pathways data is Wednesday 20 Jan 2021.
1.6 Auxiliary functions
These are functions which will be used further in the analyses.
Function to estimate the generalised R-squared as the proportion of deviance explained by a given model:
## Function to calculate R2 for Poisson model
## not adjusted for model complexity but all models have the same DF here
Rsq <- function(x) {
1 - (x$deviance / x$null.deviance)
}Function to extract growth rates per region as well as halving times, and the associated 95% confidence intervals:
## function to extract the coefficients, find the level of the intercept,
## reconstruct the values of r, get confidence intervals
get_r <- function(model) {
## extract coefficients and conf int
out <- data.frame(r = coef(model)) %>%
rownames_to_column("var") %>%
cbind(confint(model)) %>%
filter(!grepl("day_of_week", var)) %>%
filter(grepl("day", var)) %>%
rename(lower_95 = "2.5 %",
upper_95 = "97.5 %") %>%
mutate(var = sub("day:", "", var))
## reconstruct values: intercept + region-coefficient
for (i in 2:nrow(out)) {
out[i, -1] <- out[1, -1] + out[i, -1]
}
## find the name of the intercept, restore regions names
out <- out %>%
mutate(nhs_region = model$xlevels$nhs_region) %>%
select(nhs_region, everything(), -var)
## find halving times
halving <- log(0.5) / out[,-1] %>%
rename(halving_t = r,
halving_t_lower_95 = lower_95,
halving_t_upper_95 = upper_95)
## set halving times with exclusion intervals to NA
no_halving <- out$lower_95 < 0 & out$upper_95 > 0
halving[no_halving, ] <- NA_real_
## return all data
cbind(out, halving)
}Functions used in the correlation analysis between NHS Pathways reports and deaths:
## Function to calculate Pearson's correlation between deaths and lagged
## reports. Note that `pearson` can be replaced with `spearman` for rank
## correlation.
getcor <- function(x, ndx) {
return(cor(x$deaths[ndx],
x$note_lag[ndx],
use = "complete.obs",
method = "pearson"))
}
## Catch if sample size throws an error
getcor2 <- possibly(getcor, otherwise = NA)
getboot <- function(x) {
result <- boot::boot.ci(boot::boot(x, getcor2, R = 1000),
type = "bca")
return(data.frame(n = sum(!is.na(x$note_lag) & !is.na(x$deaths)),
r = result$t0,
r_low = result$bca[4],
r_hi = result$bca[5]))
}Function to classify the day of the week into weekend, Monday, and the rest:
## Fn to add day of week
day_of_week <- function(df) {
df %>%
dplyr::mutate(day_of_week = lubridate::wday(date, label = TRUE)) %>%
dplyr::mutate(day_of_week = dplyr::case_when(
day_of_week %in% c("Sat", "Sun") ~ "weekend",
day_of_week %in% c("Mon") ~ "monday",
!(day_of_week %in% c("Sat", "Sun", "Mon")) ~ "rest_of_week"
) %>%
factor(levels = c("rest_of_week", "monday", "weekend")))
}Custom color palettes, color scales, and vectors of colors:
2 Temporal trends in NHS Pathways reports
2.1 Outline
We look for temporal patterns in COVID-19 related 111/999 calls and 111 online reports. Analyses are broken down by NHS region. We also look for estimates of recent growth rate and associated doubling / halving time.
2.2 Reports by region over time
tab_date_region_all <- x %>%
filter(!is.na(nhs_region)) %>%
group_by(date, nhs_region) %>%
summarise(n = sum(count))
dth %>%
mutate(trusted = case_when(date_report < max(dth$date_report)-delay_max ~ "Y",
date_report >= max(dth$date_report)-delay_max ~ "N"),
value = "Deaths",
vline = max(dth$date_report)-delay_max-1,
lab = "Truncated for reporting delay",
lab_pos_x = vline + 10,
lab_pos_y = 150,
lab_col = "darkgrey") %>%
rename(date = date_report,
n = deaths) %>%
bind_rows(
mutate(tab_date_region_all, value = "Reports",
trusted = "Y",
vline = as.Date("2020-03-23"),
lab = "Start of UK lockdown",
lab_pos_x = vline - 8,
lab_pos_y = 30200,
lab_col = "black")
) %>%
mutate(value = factor(value, levels = c("Reports","Deaths"))) -> dths_reports
plot_dth_report <-
ggplot(dths_reports, aes(date, n, colour = nhs_region)) +
# Add main points and lines, coloured by region and fade out deaths for excluded period
geom_point(aes(alpha = trusted)) +
geom_line(alpha = 0.2) +
geom_smooth(method = "loess", span = .5, color = "black") +
scale_colour_manual("", values = pal) +
scale_alpha_manual(values = c(0.3,1)) +
guides(alpha = F) +
# Add vertical markers for important dates with labels - different for each facet
ggnewscale::new_scale_colour() +
geom_vline(aes(xintercept = vline, col = value), lty = "solid") +
geom_text(aes(x = lab_pos_x, y = lab_pos_y, label = lab, col = value), size = 3) +
scale_colour_manual("",values = c("black","darkgrey"), guide = F) +
# Facet by deaths and reports
facet_grid(rows = vars(value), scales = "free_y", switch = "y") +
# Other formatting
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",strip.placement = "outside") +
rotate_x +
labs(x = NULL,
y = NULL)
plot_dth_report
2.3 Reports by region by age over time
We plot the number of 111/999 calls and 111 online reports by age, and the proportion of 111/999 calls and 111 online reports by age. In the second graph, the vertical lines indicate the proportion of individuals residing in the corresponding NHS region who belong to the corresponding age group.
tab_date_region_age_all <- x %>%
filter(!is.na(nhs_region),
age != "missing") %>%
group_by(date, nhs_region, age) %>%
summarise(n = sum(count))
tab_date_region_age_all %>%
ggplot(aes(x = date, y = n, fill = age)) +
geom_col(position = "stack") +
scale_fill_manual(values = age.pal) +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
axis.text.x = element_text(angle = 90, hjust = 1)) +
guides(fill = guide_legend(title = "Age", ncol = 3)) +
labs(x = NULL,
y = "Total daily reports by age") +
facet_wrap(~ nhs_region, ncol = 4)
tab_date_region_age_all <- tab_date_region_age_all %>%
group_by(date, nhs_region) %>%
summarise(tot = sum(n)) %>%
left_join(tab_date_region_age_all, by = c("date", "nhs_region")) %>%
mutate(prop_n = n/tot)
tab_date_region_age_all %>%
ggplot(aes(x = date, y = prop_n, color = age)) +
scale_color_manual(values = age.pal) +
geom_line() +
geom_point() +
geom_hline(data = nhs_region_pop, aes(yintercept = value, color = variable)) +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
axis.text.x = element_text(angle = 90, hjust = 1)) +
guides(color = guide_legend(title = "Age", ncol = 3)) +
labs(x = NULL,
y = "Proportion of daily reports by age") +
facet_wrap(~ nhs_region, ncol = 4)
2.4 Moving window model
We fit quasi-Poisson GLMs for 14-day windows to get growth rates over time.
## set moving time window (1/2/3 weeks)
w <- 14
# create empty df
r_all_sliding <- NULL
## make data for model
x_model_all_moving <- x %>%
filter(!is.na(nhs_region)) %>%
group_by(date, nhs_region) %>%
summarise(n = sum(count))
unique_dates <- unique(x_model_all_moving$date)
for (i in 1:(length(unique_dates) - w)) {
date_i <- unique_dates[i]
date_i_max <- date_i + w
model_data <- x_model_all_moving %>%
filter(date >= date_i & date < date_i_max) %>%
mutate(day = as.integer(date - date_i)) %>%
day_of_week()
mod <- glm(n ~ day * nhs_region + day_of_week,
data = model_data,
family = 'quasipoisson')
# get growth rate
r <- get_r(mod)
r$w_min <- date_i
r$w_max <- date_i_max
# combine all estimates
r_all_sliding <- bind_rows(r_all_sliding, r)
}
#serial interval distribution
SI_param = epitrix::gamma_mucv2shapescale(4.7, 2.9/4.7)
SI_distribution <- distcrete::distcrete("gamma", interval = 1,
shape = SI_param$shape,
scale = SI_param$scale,
w = 0.5)
#convert growth rates r to R0
r_all_sliding <- r_all_sliding %>%
mutate(R = epitrix::r2R0(r, SI_distribution),
R_lower_95 = epitrix::r2R0(lower_95, SI_distribution),
R_upper_95 = epitrix::r2R0(upper_95, SI_distribution))We examine the evolution of the growth rate by region over time.
# plot
plot_growth <-
r_all_sliding %>%
ggplot(aes(x = w_max, y = r)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 0, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(colour = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated daily growth rate (r)") +
scale_colour_manual(values = pal)From the growth rate, we derive R and examine its value through time.
# plot
plot_R <-
r_all_sliding %>%
ggplot(aes(x = w_max, y = R)) +
geom_ribbon(aes(ymin = R_lower_95, ymax = R_upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 1, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated effective reproduction\nnumber (Re)") +
scale_colour_manual(values = pal)
R <- r_all_sliding %>%
mutate(lower_95 = R_lower_95,
upper_95 = R_upper_95,
value = R,
measure = "R",
reference = 1)
r_R <- r_all_sliding %>%
mutate(measure = "r",
value = r,
reference = 0) %>%
bind_rows(R)
r_R %>%
ggplot(aes(x = w_max, y = value)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(aes(yintercept = reference), linetype = "dashed") +
theme_bw() +
scale_weeks +
rotate_x +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0,0, "cm"),
strip.background = element_blank(),
# strip.text.x = element_blank(),
strip.placement = "outside"
) +
guides(color = guide_legend(title = "",
override.aes = list(fill = NA)),
fill = FALSE) +
labs(x = "", y = "") +
scale_colour_manual(values = pal) +
facet_grid(rows = vars(measure),
scales = "free_y",
switch = "y",
labeller = as_labeller(c(r = "Daily growth rate (r)",
R = "Effective reproduction\nnumber (Re)")))
2.5 Moving window model - stratified by age
We repeat the above analysis, where we fit quasi-Poisson GLMs for 14-day windows to get growth rates over time, but apply this to each age group separately (0-18, 19-69, 70-120 years old).
We first run the analysis for 0-18 years old.
## set moving time window (2 weeks)
w <- 14
# create empty df
r_all_sliding_0_18 <- NULL
## make data for model
x_model_all_moving_0_18 <- x %>%
filter(!is.na(nhs_region),
age == "0-18") %>%
group_by(date, nhs_region) %>%
summarise(n = sum(count))
unique_dates <- unique(x_model_all_moving_0_18$date)
for (i in 1:(length(unique_dates) - w)) {
date_i <- unique_dates[i]
date_i_max <- date_i + w
model_data <- x_model_all_moving_0_18 %>%
filter(date >= date_i & date < date_i_max) %>%
mutate(day = as.integer(date - date_i)) %>%
day_of_week()
mod <- glm(n ~ day * nhs_region + day_of_week,
data = model_data,
family = 'quasipoisson')
# get growth rate
r <- get_r(mod)
r$w_min <- date_i
r$w_max <- date_i_max
# combine all estimates
r_all_sliding_0_18 <- bind_rows(r_all_sliding_0_18, r)
}
#serial interval distribution
SI_param = epitrix::gamma_mucv2shapescale(4.7, 2.9/4.7)
SI_distribution <- distcrete::distcrete("gamma", interval = 1,
shape = SI_param$shape,
scale = SI_param$scale, w = 0.5)
#convert growth rates r to R0
r_all_sliding_0_18 <- r_all_sliding_0_18 %>%
mutate(R = epitrix::r2R0(r, SI_distribution),
R_lower_95 = epitrix::r2R0(lower_95, SI_distribution),
R_upper_95 = epitrix::r2R0(upper_95, SI_distribution))# plot
plot_growth <-
r_all_sliding_0_18 %>%
ggplot(aes(x = w_max, y = r)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 0, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(colour = guide_legend(title = "",override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated daily growth rate (r)"
) +
scale_colour_manual(values = pal)# plot
plot_R <-
r_all_sliding_0_18 %>%
ggplot(aes(x = w_max, y = R)) +
geom_ribbon(aes(ymin = R_lower_95, ymax = R_upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 1, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated effective reproduction\nnumber (Re)"
) +
scale_colour_manual(values = pal)
R <- r_all_sliding_0_18 %>%
mutate(lower_95 = R_lower_95,
upper_95 = R_upper_95,
value = R,
measure = "R",
reference = 1)
r_R <- r_all_sliding_0_18 %>%
mutate(measure = "r",
value = r,
reference = 0) %>%
bind_rows(R)
fig2_3_0_18 <- r_R %>%
ggplot(aes(x = w_max, y = value)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(aes(yintercept = reference), linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0,0, "cm"),
strip.background = element_blank(),
strip.placement = "outside"
) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "", y = "") +
scale_colour_manual(values = pal) +
facet_grid(rows = vars(measure),
scales = "free_y",
switch = "y",
labeller = as_labeller(c(r = "Daily growth rate (r)",
R = "Effective reproduction\nnumber (Re)")))Then, we run the analysis for 19-69 years old.
## set moving time window (2 weeks)
w <- 14
# create empty df
r_all_sliding_19_69 <- NULL
## make data for model
x_model_all_moving_19_69 <- x %>%
filter(!is.na(nhs_region),
age == "19-69") %>%
group_by(date, nhs_region) %>%
summarise(n = sum(count))
unique_dates <- unique(x_model_all_moving_19_69$date)
for (i in 1:(length(unique_dates) - w)) {
date_i <- unique_dates[i]
date_i_max <- date_i + w
model_data <- x_model_all_moving_19_69 %>%
filter(date >= date_i & date < date_i_max) %>%
mutate(day = as.integer(date - date_i)) %>%
day_of_week()
mod <- glm(n ~ day * nhs_region + day_of_week,
data = model_data,
family = 'quasipoisson')
# get growth rate
r <- get_r(mod)
r$w_min <- date_i
r$w_max <- date_i_max
# combine all estimates
r_all_sliding_19_69 <- bind_rows(r_all_sliding_19_69, r)
}
#serial interval distribution
SI_param = epitrix::gamma_mucv2shapescale(4.7, 2.9/4.7)
SI_distribution <- distcrete::distcrete("gamma", interval = 1,
shape = SI_param$shape,
scale = SI_param$scale, w = 0.5)
#convert growth rates r to R0
r_all_sliding_19_69 <- r_all_sliding_19_69 %>%
mutate(R = epitrix::r2R0(r, SI_distribution),
R_lower_95 = epitrix::r2R0(lower_95, SI_distribution),
R_upper_95 = epitrix::r2R0(upper_95, SI_distribution))# plot
plot_growth <-
r_all_sliding_19_69 %>%
ggplot(aes(x = w_max, y = r)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 0, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(colour = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated daily growth rate (r)") +
scale_colour_manual(values = pal)# plot
plot_R <-
r_all_sliding_19_69 %>%
ggplot(aes(x = w_max, y = R)) +
geom_ribbon(aes(ymin = R_lower_95, ymax = R_upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 1, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated effective reproduction\nnumber (Re)"
) +
scale_colour_manual(values = pal)
R <- r_all_sliding_19_69 %>%
mutate(lower_95 = R_lower_95,
upper_95 = R_upper_95,
value = R,
measure = "R",
reference = 1)
r_R <- r_all_sliding_19_69 %>%
mutate(measure = "r",
value = r,
reference = 0) %>%
bind_rows(R)
fig2_3_19_69 <- r_R %>%
ggplot(aes(x = w_max, y = value)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(aes(yintercept = reference), linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0,0, "cm"),
strip.background = element_blank(),
strip.placement = "outside"
) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "", y = "") +
scale_colour_manual(values = pal) +
facet_grid(rows = vars(measure),
scales = "free_y",
switch = "y",
labeller = as_labeller(c(r = "Daily growth rate (r)",
R = "Effective reproduction\nnumber (Re)")))Finally, we run the analysis for 70-120 years old.
## set moving time window (2 weeks)
w <- 14
# create empty df
r_all_sliding_70_120 <- NULL
## make data for model
x_model_all_moving_70_120 <- x %>%
filter(!is.na(nhs_region),
age == "70-120") %>%
group_by(date, nhs_region) %>%
summarise(n = sum(count))
unique_dates <- unique(x_model_all_moving_70_120$date)
for (i in 1:(length(unique_dates) - w)) {
date_i <- unique_dates[i]
date_i_max <- date_i + w
model_data <- x_model_all_moving_70_120 %>%
filter(date >= date_i & date < date_i_max) %>%
mutate(day = as.integer(date - date_i)) %>%
day_of_week()
mod <- glm(n ~ day * nhs_region + day_of_week,
data = model_data,
family = 'quasipoisson')
# get growth rate
r <- get_r(mod)
r$w_min <- date_i
r$w_max <- date_i_max
# combine all estimates
r_all_sliding_70_120 <- bind_rows(r_all_sliding_70_120, r)
}
#serial interval distribution
SI_param = epitrix::gamma_mucv2shapescale(4.7, 2.9/4.7)
SI_distribution <- distcrete::distcrete("gamma", interval = 1,
shape = SI_param$shape,
scale = SI_param$scale, w = 0.5)
#convert growth rates r to R0
r_all_sliding_70_120 <- r_all_sliding_70_120 %>%
mutate(R = epitrix::r2R0(r, SI_distribution),
R_lower_95 = epitrix::r2R0(lower_95, SI_distribution),
R_upper_95 = epitrix::r2R0(upper_95, SI_distribution))# plot
plot_growth <-
r_all_sliding_70_120 %>%
ggplot(aes(x = w_max, y = r)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 0, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(colour = guide_legend(title = "",override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated daily growth rate (r)"
) +
scale_colour_manual(values = pal)# plot
plot_R <-
r_all_sliding_70_120 %>%
ggplot(aes(x = w_max, y = R)) +
geom_ribbon(aes(ymin = R_lower_95, ymax = R_upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 1, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated effective reproduction\nnumber (Re)") +
scale_colour_manual(values = pal)
R <- r_all_sliding_70_120 %>%
mutate(lower_95 = R_lower_95,
upper_95 = R_upper_95,
value = R,
measure = "R",
reference = 1)
r_R <- r_all_sliding_70_120 %>%
mutate(measure = "r",
value = r,
reference = 0) %>%
bind_rows(R)
fig2_3_70_120 <- r_R %>%
ggplot(aes(x = w_max, y = value)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(aes(yintercept = reference), linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0,0, "cm"),
strip.background = element_blank(),
strip.placement = "outside"
) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "", y = "") +
scale_colour_manual(values = pal) +
facet_grid(rows = vars(measure),
scales = "free_y",
switch = "y",
labeller = as_labeller(c(r = "Daily growth rate (r)",
R = "Effective reproduction\nnumber (Re)"))) We combine the estimated growth rates and effective reproduction numbers into a single figure.
ggpubr::ggarrange(fig2_3_0_18,
fig2_3_19_69,
fig2_3_70_120,
nrow = 3,
labels = "AUTO",
common.legend = TRUE,
legend = "bottom",
align = "hv") 
3 Comparison with deaths time series
3.1 Outline
We want to explore the correlation between NHS Pathways reports and deaths, and assess the potential for reports to be used as an early warning system for disease resurgence.
Death data are publically available. We truncate the time series to avoid bias from reporting delay - we assume a conservative delay of three weeks.
3.2 Lagged correlation
We calculate Pearson’s correlation coefficient between deaths and NHS Pathways notifications using different lags. Confidence intervals are obtained using bootstrap. Note that results were also confirmed using Spearman’s rank correlation.
First we join the NHS Pathways and death data, and aggregate over all England:
## truncate death data for reporting delay
trunc_date <- max(dth$date_report) - delay_max
dth_trunc <- dth %>%
rename(date = date_report) %>%
filter(date <= trunc_date)
## join with notification data
all_data <- x %>%
filter(!is.na(nhs_region)) %>%
group_by(date, nhs_region) %>%
summarise(count = sum(count, na.rm = T)) %>%
ungroup %>%
inner_join(dth_trunc,
by = c("date","nhs_region"))
all_tot <- all_data %>%
group_by(date) %>%
summarise(count = sum(count, na.rm = TRUE),
deaths = sum(deaths, na.rm = TRUE)) We calculate correlation with lagged NHS Pathways reports from 0 to 30 days behind deaths:
## Calculate all correlations + bootstrap CIs
lag_cor <- data.frame()
for (i in 0:30) {
## lag reports
summary <- all_tot %>%
mutate(note_lag = lag(count, i)) %>%
## calculate rank correlation and bootstrap CI
getboot(.) %>%
mutate(lag = i)
lag_cor <- bind_rows(lag_cor, summary)
}
cor_vs_lag <- ggplot(lag_cor, aes(lag, r)) +
theme_bw() +
geom_ribbon(aes(ymin = r_low, ymax = r_hi), alpha = 0.2) +
geom_hline(yintercept = 0, lty = "longdash") +
geom_point() +
geom_line() +
labs(x = "Lag between NHS pathways and death data (days)",
y = "Pearson's correlation") +
large_txt
cor_vs_lag
This analysis suggests that the best lag is 16 days. We then compare and plot the number of deaths reported against the number of NHS Pathways reports lagged by 16 days.
all_tot <- all_tot %>%
rename(date_death = date) %>%
mutate(note_lag = lag(count, lag_cor$lag[l_opt]),
note_lag_c = (note_lag - mean(note_lag, na.rm = T)),
date_note = lag(date_death,16))
lag_mod <- glm(deaths ~ note_lag, data = all_tot, family = "quasipoisson")
summary(lag_mod)
##
## Call:
## glm(formula = deaths ~ note_lag, family = "quasipoisson", data = all_tot)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -14.662 -12.162 -4.521 8.172 22.326
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.706e+00 6.422e-02 73.28 <2e-16 ***
## note_lag 1.537e-05 9.485e-07 16.21 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for quasipoisson family taken to be 113.53)
##
## Null deviance: 53017 on 270 degrees of freedom
## Residual deviance: 31722 on 269 degrees of freedom
## (16 observations deleted due to missingness)
## AIC: NA
##
## Number of Fisher Scoring iterations: 5
exp(coefficients(lag_mod))
## (Intercept) note_lag
## 110.600553 1.000015
exp(confint(lag_mod))
## 2.5 % 97.5 %
## (Intercept) 97.295120 125.159099
## note_lag 1.000013 1.000017
Rsq(lag_mod)
## [1] 0.4016584
mod_fit <- as.data.frame(predict(lag_mod, type = "link", se.fit = TRUE)[1:2])
all_tot_pred <-
all_tot %>%
filter(!is.na(note_lag)) %>%
mutate(pred = mod_fit$fit,
pred.se = mod_fit$se.fit,
low = exp(pred - 1.96*pred.se),
hi = exp(pred + 1.96*pred.se))
glm_fit <- all_tot_pred %>%
filter(!is.na(note_lag)) %>%
ggplot(aes(x = note_lag, y = deaths)) +
geom_point() +
geom_line(aes(y = exp(pred))) +
geom_ribbon(aes(ymin = low, ymax = hi), alpha = 0.3, col = "grey") +
theme_bw() +
labs(y = "Daily number of\ndeaths reported",
x = "Daily number of NHS Pathways reports") +
large_txt
glm_fit
4 Supplementary figures
4.1 Serial interval distribution
This is a comparison of gamma versus lognormal distribution for the serial interval used to convert r to R in our analysis. Both distributions are parameterised with mean 4.7 and standard deviation 2.9.
SI_param <- epitrix::gamma_mucv2shapescale(4.7, 2.9/4.7)
SI_distribution <- distcrete::distcrete("gamma", interval = 1,
shape = SI_param$shape,
scale = SI_param$scale, w = 0.5)
SI_distribution2 <- distcrete::distcrete("lnorm", interval = 1,
meanlog = log(4.7),
sdlog = log(2.9), w = 0.5)
SI_dist1 <- data.frame(x = SI_distribution$r(1e5))
SI_dist1 <- count(SI_dist1, x) %>%
ggplot() +
geom_col(aes(x = x, y = n)) +
labs(x = "Serial interval (days)", y = "Frequency") +
scale_x_continuous(breaks = seq(0, 30, 5)) +
theme_bw()
SI_dist2 <- data.frame(x = SI_distribution2$r(1e5))
SI_dist2 <- count(SI_dist2, x) %>%
ggplot() +
geom_col(aes(x = x, y = n)) +
labs(x = "Serial interval (days)", y = "Frequency") +
scale_x_continuous(breaks = seq(0, 200, 20), limits = c(0, 200)) +
theme_bw()
ggpubr::ggarrange(SI_dist1,
SI_dist2,
nrow = 1,
labels = "AUTO") 
4.2 Sensitivity analysis - 7 or 21 days moving window
We reproduce the window analysis with either a 7 or 21 days window for sensitivity purposes.
First with the 7 days window:
## set moving time window (1/2/3 weeks)
w <- 7
# create empty df
r_all_sliding_7days <- NULL
## make data for model
x_model_all_moving <- x %>%
filter(!is.na(nhs_region)) %>%
group_by(date, nhs_region) %>%
summarise(n = sum(count))
unique_dates <- unique(x_model_all_moving$date)
for (i in 1:(length(unique_dates) - w)) {
date_i <- unique_dates[i]
date_i_max <- date_i + w
model_data <- x_model_all_moving %>%
filter(date >= date_i & date < date_i_max) %>%
mutate(day = as.integer(date - date_i)) %>%
day_of_week()
mod <- glm(n ~ day * nhs_region + day_of_week,
data = model_data,
family = 'quasipoisson')
# get growth rate
r <- get_r(mod)
r$w_min <- date_i
r$w_max <- date_i_max
# combine all estimates
r_all_sliding_7days <- bind_rows(r_all_sliding_7days, r)
}
#serial interval distribution
SI_param = epitrix::gamma_mucv2shapescale(4.7, 2.9/4.7)
SI_distribution <- distcrete::distcrete("gamma", interval = 1,
shape = SI_param$shape,
scale = SI_param$scale,
w = 0.5)
#convert growth rates r to R0
r_all_sliding_7days <- r_all_sliding_7days %>%
mutate(R = epitrix::r2R0(r, SI_distribution),
R_lower_95 = epitrix::r2R0(lower_95, SI_distribution),
R_upper_95 = epitrix::r2R0(upper_95, SI_distribution))# plot
plot_growth <-
r_all_sliding_7days %>%
ggplot(aes(x = w_max, y = r)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 0, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(colour = guide_legend(title = "",override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated daily growth rate (r)") +
scale_colour_manual(values = pal)plot_R <- r_all_sliding_7days %>%
ggplot(aes(x = w_max, y = R)) +
geom_ribbon(aes(ymin = R_lower_95, ymax = R_upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 1, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated effective reproduction\nnumber (Re)") +
scale_colour_manual(values = pal)
R <- r_all_sliding_7days %>%
mutate(lower_95 = R_lower_95,
upper_95 = R_upper_95,
value = R,
measure = "R",
reference = 1)
r_R <- r_all_sliding_7days %>%
mutate(measure = "r",
value = r,
reference = 0) %>%
bind_rows(R)
r_R_7 <- r_R %>%
ggplot(aes(x = w_max, y = value)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(aes(yintercept = reference), linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0,0, "cm"),
strip.background = element_blank(),
strip.placement = "outside"
) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "", y = "") +
scale_colour_manual(values = pal) +
facet_grid(rows = vars(measure),
scales = "free_y",
switch = "y",
labeller = as_labeller(c(r = "Daily growth rate (r)",
R = "Effective reproduction\nnumber (Re)")))Then with the 21 days window:
## set moving time window (1/2/3 weeks)
w <- 21
# create empty df
r_all_sliding_21days <- NULL
## make data for model
x_model_all_moving <- x %>%
filter(!is.na(nhs_region)) %>%
group_by(date, nhs_region) %>%
summarise(n = sum(count))
unique_dates <- unique(x_model_all_moving$date)
for (i in 1:(length(unique_dates) - w)) {
date_i <- unique_dates[i]
date_i_max <- date_i + w
model_data <- x_model_all_moving %>%
filter(date >= date_i & date < date_i_max) %>%
mutate(day = as.integer(date - date_i)) %>%
day_of_week()
mod <- glm(n ~ day * nhs_region + day_of_week,
data = model_data,
family = 'quasipoisson')
# get growth rate
r <- get_r(mod)
r$w_min <- date_i
r$w_max <- date_i_max
# combine all estimates
r_all_sliding_21days <- bind_rows(r_all_sliding_21days, r)
}
#serial interval distribution
SI_param = epitrix::gamma_mucv2shapescale(4.7, 2.9/4.7)
SI_distribution <- distcrete::distcrete("gamma", interval = 1,
shape = SI_param$shape,
scale = SI_param$scale,
w = 0.5)
#convert growth rates r to R0
r_all_sliding_21days <- r_all_sliding_21days %>%
mutate(R = epitrix::r2R0(r, SI_distribution),
R_lower_95 = epitrix::r2R0(lower_95, SI_distribution),
R_upper_95 = epitrix::r2R0(upper_95, SI_distribution))# plot
plot_growth <-
r_all_sliding_21days %>%
ggplot(aes(x = w_max, y = r)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 0, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(colour = guide_legend(title = "",override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated daily growth rate (r)") +
scale_colour_manual(values = pal)# plot
plot_R <-
r_all_sliding_21days %>%
ggplot(aes(x = w_max, y = R)) +
geom_ribbon(aes(ymin = R_lower_95, ymax = R_upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 1, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated effective reproduction\nnumber (Re)") +
scale_colour_manual(values = pal)
R <- r_all_sliding_21days %>%
mutate(lower_95 = R_lower_95,
upper_95 = R_upper_95,
value = R,
measure = "R",
reference = 1)
r_R <- r_all_sliding_21days %>%
mutate(measure = "r",
value = r,
reference = 0) %>%
bind_rows(R)
r_R_21 <- r_R %>%
ggplot(aes(x = w_max, y = value)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(aes(yintercept = reference), linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0,0, "cm"),
strip.background = element_blank(),
strip.placement = "outside"
) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "", y = "") +
scale_colour_manual(values = pal) +
facet_grid(rows = vars(measure),
scales = "free_y",
switch = "y",
labeller = as_labeller(c(r = "Daily growth rate (r)",
R = "Effective reproduction\nnumber (Re)")))And we combine both outputs into a single plot:
ggpubr::ggarrange(r_R_7,
r_R_21,
nrow = 2,
labels = "AUTO",
common.legend = TRUE,
legend = "bottom") 
4.3 Correlation between NHS Pathways reports and deaths by NHS region
lag_cor_reg <- data.frame()
for (i in 0:30) {
summary <-
all_data %>%
group_by(nhs_region) %>%
mutate(note_lag = lag(count, i)) %>%
## calculate rank correlation and bootstrap CI for each region
group_modify(~getboot(.x)) %>%
mutate(lag = i)
lag_cor_reg <- bind_rows(lag_cor_reg, summary)
}
cor_vs_lag_reg <-
lag_cor_reg %>%
ggplot(aes(lag, r, col = nhs_region)) +
geom_hline(yintercept = 0, lty = "longdash") +
geom_ribbon(aes(ymin = r_low, ymax = r_hi, col = NULL, fill = nhs_region), alpha = 0.2) +
geom_point() +
geom_line() +
facet_wrap(~nhs_region) +
scale_color_manual(values = pal) +
scale_fill_manual(values = pal, guide = F) +
theme_bw() +
labs(x = "Lag between NHS pathways and death data (days)", y = "Pearson's correlation", col = "NHS region") +
theme(legend.position = "bottom") +
guides(color = guide_legend(override.aes = list(fill = NA)))
cor_vs_lag_reg
5 Export data
We save the tables created during our analysis:
if (!dir.exists("excel_tables")) {
dir.create("excel_tables")
}
## list all tables, and loop over export
tables_to_export <- c("r_all_sliding", "lag_cor")
for (e in tables_to_export) {
rio::export(get(e),
file.path("excel_tables",
paste0(e, ".xlsx")))
}
## also export result from regression on lagged data
rio::export(lag_mod, file.path("excel_tables", "lag_mod.rds"))6 System information
6.1 Outline
The following information documents the system on which the document was compiled.
6.2 System
This provides information on the operating system.
Sys.info()
## sysname
## "Darwin"
## release
## "19.6.0"
## version
## "Darwin Kernel Version 19.6.0: Thu Oct 29 22:56:45 PDT 2020; root:xnu-6153.141.2.2~1/RELEASE_X86_64"
## nodename
## "Mac-1611227864771.local"
## machine
## "x86_64"
## login
## "root"
## user
## "runner"
## effective_user
## "runner"6.3 R environment
This provides information on the version of R used:
6.4 R packages
This provides information on the packages used:
sessionInfo()
## R version 4.0.3 (2020-10-10)
## Platform: x86_64-apple-darwin17.0 (64-bit)
## Running under: macOS Catalina 10.15.7
##
## Matrix products: default
## BLAS: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRblas.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRlapack.dylib
##
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] ggnewscale_0.4.5 ggpubr_0.4.0 lubridate_1.7.9.2
## [4] chngpt_2020.10-12 cyphr_1.1.0 DT_0.17
## [7] kableExtra_1.3.1 janitor_2.1.0 remotes_2.2.0
## [10] projections_0.5.2 earlyR_0.0.5 epitrix_0.2.2
## [13] distcrete_1.0.3 incidence_1.7.3 rio_0.5.16
## [16] reshape2_1.4.4 rvest_0.3.6 xml2_1.3.2
## [19] linelist_0.0.40.9000 forcats_0.5.0 stringr_1.4.0
## [22] dplyr_1.0.3 purrr_0.3.4 readr_1.4.0
## [25] tidyr_1.1.2 tibble_3.0.5 ggplot2_3.3.3
## [28] tidyverse_1.3.0 here_1.0.1 reportfactory_0.0.5
##
## loaded via a namespace (and not attached):
## [1] minqa_1.2.4 colorspace_2.0-0 selectr_0.4-2 ggsignif_0.6.0
## [5] ellipsis_0.3.1 rprojroot_2.0.2 snakecase_0.11.0 fs_1.5.0
## [9] rstudioapi_0.13 farver_2.0.3 fansi_0.4.2 splines_4.0.3
## [13] knitr_1.30 jsonlite_1.7.2 nloptr_1.2.2.2 broom_0.7.3
## [17] dbplyr_2.0.0 compiler_4.0.3 httr_1.4.2 backports_1.2.1
## [21] assertthat_0.2.1 Matrix_1.2-18 cli_2.2.0 htmltools_0.5.1
## [25] tools_4.0.3 gtable_0.3.0 glue_1.4.2 Rcpp_1.0.6
## [29] carData_3.0-4 cellranger_1.1.0 vctrs_0.3.6 nlme_3.1-149
## [33] matchmaker_0.1.1 crosstalk_1.1.1 xfun_0.20 ps_1.5.0
## [37] openxlsx_4.2.3 lme4_1.1-26 lifecycle_0.2.0 statmod_1.4.35
## [41] rstatix_0.6.0 MASS_7.3-53 scales_1.1.1 hms_1.0.0
## [45] parallel_4.0.3 sodium_1.1 yaml_2.2.1 curl_4.3
## [49] gridExtra_2.3 stringi_1.5.3 kyotil_2020.10-12 boot_1.3-25
## [53] zip_2.1.1 rlang_0.4.10 pkgconfig_2.0.3 evaluate_0.14
## [57] lattice_0.20-41 labeling_0.4.2 htmlwidgets_1.5.3 cowplot_1.1.1
## [61] tidyselect_1.1.0 plyr_1.8.6 magrittr_2.0.1 R6_2.5.0
## [65] generics_0.1.0 DBI_1.1.1 pillar_1.4.7 haven_2.3.1
## [69] foreign_0.8-80 withr_2.4.0 mgcv_1.8-33 survival_3.2-7
## [73] abind_1.4-5 modelr_0.1.8 crayon_1.3.4 car_3.0-10
## [77] utf8_1.1.4 rmarkdown_2.6 viridis_0.5.1 grid_4.0.3
## [81] readxl_1.3.1 data.table_1.13.6 reprex_0.3.0 digest_0.6.27
## [85] webshot_0.5.2 munsell_0.5.0 viridisLite_0.3.0